File size: 88,383 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 |
{
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T04:33:41.572560Z"
},
"title": "Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches",
"authors": [
{
"first": "Shagun",
"middle": [],
"last": "Sinha",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Nehru University",
"location": {
"settlement": "New Delhi"
}
},
"email": "[email protected]"
},
{
"first": "Girish",
"middle": [],
"last": "Nath",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Nehru University",
"location": {
"settlement": "New Delhi"
}
},
"email": "[email protected]"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "The authors present a work-in-progress in the eld of Abstractive Text Summarization (ATS) for Sanskrit Prose-a rst attempt at ATS for Sanskrit (SATS). We will evaluate recent approaches and methods used for ATS and argue for the ones to be adopted for Sanskrit prose considering the unique properties of the language. There are three goals of SATS-to make manuscript summaries, to enrich the semantic processing of Sanskrit, and to improve the information retrieval systems in the language. While Extractive Text Summarization (ETS) is an important method, the summaries it generates are not always coherent. For qualitative coherent summaries, ATS is considered a better option by scholars. This paper reviews various ATS/ETS approaches for Sanskrit and other Indian Languages done till date. In the preliminary overview, authors conclude that of the two available approaches-structure-based and semantics-based-the latter would be viable owing to the rich morphology of Sanskrit. Moreover, a graph-based method may also be suitable. The second suggested method is the supervised-learning method. The authors also suggest attempting cross-lingual summarization as an extension to this work in future.",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "The authors present a work-in-progress in the eld of Abstractive Text Summarization (ATS) for Sanskrit Prose-a rst attempt at ATS for Sanskrit (SATS). We will evaluate recent approaches and methods used for ATS and argue for the ones to be adopted for Sanskrit prose considering the unique properties of the language. There are three goals of SATS-to make manuscript summaries, to enrich the semantic processing of Sanskrit, and to improve the information retrieval systems in the language. While Extractive Text Summarization (ETS) is an important method, the summaries it generates are not always coherent. For qualitative coherent summaries, ATS is considered a better option by scholars. This paper reviews various ATS/ETS approaches for Sanskrit and other Indian Languages done till date. In the preliminary overview, authors conclude that of the two available approaches-structure-based and semantics-based-the latter would be viable owing to the rich morphology of Sanskrit. Moreover, a graph-based method may also be suitable. The second suggested method is the supervised-learning method. The authors also suggest attempting cross-lingual summarization as an extension to this work in future.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Text Summarization (TS) is a core area of study under Computational Linguistics (CL) and Natural Language Processing (NLP) for generation of coherent text summaries. One of the earliest works was by Luhn (1958) from IBM where he proposed to create summaries of the abstracts of scienti3c papers. TS has also been developed for a number of Indian Languages (ILs). Extractive text summarization (ETS) and abstractive text summarization (ATS) are two primary approaches that focus on summarizing IL internet content, newspaper articles, research papers, o6cial documents etc (Sankar et al., 2011; Embar et al., 2013; Talukder et al., 2019; Gupta & Lehal, 2011 ; so on). Sanskrit is studied in various forms today mostly as a compositional language preserving several million texts of great intellectual value. The issues of text availability, readability and the need to access the knowledge in it have presented a huge requirement for ATS and related research for Sanskrit. The capacity of Sanskrit to in3nitely condense an expression with recurrent usage of concatenating techniques like euphonic combinations (sandhi), compounding (samasa), scrambling, verb elision for prosody etc make it di6cult to arrive at the structural or collocational meaning of the expression. When creating summaries, it is important that the semantics is processed well. Doing a good ATS for Sanskrit thus becomes extremely challenging. Summarization can be categorized di?erently on different bases: Single versus multi-document (based on the number of documents (Jones, 1999) , textual versus multimedia (based on the mode of document), extractive versus abstractive (based on the mode of the output (Afantenos et al, 2005; Moawad & Aref, 2012) . This paper is the description of an ongoing work on Sanskrit ATS (SATS) by the authors. The main contribution of this paper lies in its surveying the existing approaches to TS done for Sanskrit till date and to look at some challenges in processing Sanskrit for ATS. The paper proposes a semantic approach for any deeper processing of the texts in the language. The authors focus on single document summarization only because a multi-document ATS may be more complex due to various factors like semantic relatedness, diversity of subject matter, size etc.",
"cite_spans": [
{
"start": 199,
"end": 210,
"text": "Luhn (1958)",
"ref_id": "BIBREF18"
},
{
"start": 572,
"end": 593,
"text": "(Sankar et al., 2011;",
"ref_id": "BIBREF28"
},
{
"start": 594,
"end": 613,
"text": "Embar et al., 2013;",
"ref_id": "BIBREF7"
},
{
"start": 614,
"end": 636,
"text": "Talukder et al., 2019;",
"ref_id": "BIBREF32"
},
{
"start": 637,
"end": 656,
"text": "Gupta & Lehal, 2011",
"ref_id": "BIBREF10"
},
{
"start": 1542,
"end": 1555,
"text": "(Jones, 1999)",
"ref_id": "BIBREF14"
},
{
"start": 1680,
"end": 1703,
"text": "(Afantenos et al, 2005;",
"ref_id": "BIBREF1"
},
{
"start": 1704,
"end": 1724,
"text": "Moawad & Aref, 2012)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1."
},
{
"text": "The origin and development of TS was inspired by the need to turn long English scienti3c texts into shorter ones (Luhn, 1958) . Currently, most ideas around TS techniques under Natural Language Processing are based on the growth of the internet and the need to condense information therein (Sunitha et. al., 2016) . In this backdrop, it is important to make one observation. While Sanskrit prose content on the net needs to be summarized as well, there are two key objectives of SATS which are di?erent from those of TS in any other language of the present day:",
"cite_spans": [
{
"start": 113,
"end": 125,
"text": "(Luhn, 1958)",
"ref_id": "BIBREF18"
},
{
"start": 290,
"end": 313,
"text": "(Sunitha et. al., 2016)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation for Sanskrit ATS",
"sec_num": "2."
},
{
"text": "\u2022 A large body of scienti3c literature is available in Sanskrit and a lot of it is in the manuscript (MS) form. The study of an MS is a far more complex and tedious process which involves editing and re-editing a historical document till the authentic content is achieved.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation for Sanskrit ATS",
"sec_num": "2."
},
{
"text": "\u2022 SATS will require semantic analysis. This could pave the way for better semantic processing of Sanskrit. Since ATS works on the principle of 'key essence' of the text rather than extracting the suitable sentences, it could help enhance algo-rithms for processing the relative meaning of the words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation for Sanskrit ATS",
"sec_num": "2."
},
{
"text": "Sanskrit TS so far has explored the extractive aspect only. Barve et.al. (2015) use three TS approaches to obtain text summary for Sanskrit based on a query given by the user -Average Term Frequency-Inverse Sentence Frequency (tf-isf), Vector Space Model (VSM), and Graph-Based Approach. They concluded that the VSM produced the best summary with 80% accuracy. ETS is a good approach for prose that has a high frequency of the query-word, as is seen in Barve et. al (2015) . However, not all prose may yield such results. In most cases, the keyword is not always repeated but is indicated through pronouns. While query-directed extraction can be highly successful in the former, it may not be so for the latter. Besides, the ETS also faces the incoherence disadvantage as mentioned by Mishra & Gayen (2018) . Abstractive approach, on the other hand, is more likely to resolve this. It 'abstracts' the essence from the text to be summarized. This leads to complexity in language processing but once successful, can result in enhanced summary quality with natural text generation. Scholars suggest that non-extractive methods generate better summaries because they reduce the information loss (Mishra & Gayen, 2018) . ATS has also been found better than ETS in other work (Giuseppe & Jackie, 2008) .",
"cite_spans": [
{
"start": 60,
"end": 79,
"text": "Barve et.al. (2015)",
"ref_id": null
},
{
"start": 453,
"end": 472,
"text": "Barve et. al (2015)",
"ref_id": "BIBREF3"
},
{
"start": 785,
"end": 806,
"text": "Mishra & Gayen (2018)",
"ref_id": "BIBREF22"
},
{
"start": 1191,
"end": 1213,
"text": "(Mishra & Gayen, 2018)",
"ref_id": "BIBREF22"
},
{
"start": 1270,
"end": 1295,
"text": "(Giuseppe & Jackie, 2008)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Literature Survey",
"sec_num": "3."
},
{
"text": "Scholars have di?erent bases for organizing the types of TS. Most of them can come under one or more of these categories:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Major ATS approaches for Indian Languages:",
"sec_num": "3.1."
},
{
"text": "1. Structure vs Semantic approach (Sunitha C et al., 2016) , 2. Machine Learning (ML) based methods (Anh & Trang, 2019; Talukder et al., 2019) , and 3. Corpora based approach (Hasler et al., 2003) 3.1.1. Sunitha C et. al. (2016) present a survey of the current techniques in ATS for ILs. Key approaches to ATS in ILs can be divided into two categories: Structurebased and Semantics based. Some notable works in ILs include Rich Semantic Graph approach for Hindi (Subramaniam & Dalal, 2015) , Malayalam (Kabeer & Idicula, 2014) , ATS through an extractive approach for Kannada (Kallimani et. el, 2014) . Structure-based approaches require the source text sentences to be collected in a prede3ned structure (Sunitha et al, 2016) . The types of structures mentioned are Tree-based, Ontology-based, Lead and Phrase structure based, Rule based and Templatebased. Each of these methods aims to collect the sentences from the source text and then generate a summary later.",
"cite_spans": [
{
"start": 34,
"end": 58,
"text": "(Sunitha C et al., 2016)",
"ref_id": "BIBREF5"
},
{
"start": 100,
"end": 119,
"text": "(Anh & Trang, 2019;",
"ref_id": "BIBREF2"
},
{
"start": 120,
"end": 142,
"text": "Talukder et al., 2019)",
"ref_id": "BIBREF32"
},
{
"start": 175,
"end": 196,
"text": "(Hasler et al., 2003)",
"ref_id": "BIBREF12"
},
{
"start": 204,
"end": 228,
"text": "Sunitha C et. al. (2016)",
"ref_id": "BIBREF5"
},
{
"start": 462,
"end": 489,
"text": "(Subramaniam & Dalal, 2015)",
"ref_id": "BIBREF31"
},
{
"start": 502,
"end": 526,
"text": "(Kabeer & Idicula, 2014)",
"ref_id": "BIBREF16"
},
{
"start": 576,
"end": 600,
"text": "(Kallimani et. el, 2014)",
"ref_id": null
},
{
"start": 705,
"end": 726,
"text": "(Sunitha et al, 2016)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Major ATS approaches for Indian Languages:",
"sec_num": "3.1."
},
{
"text": "In the Semantics based approach, there are three phases that lead to the summary-document input, semantic review and representation and then 3nally summary based on this semantic representation through Natural Language Generation (Sunitha et al., 2016) . Multimodal semantic, Information Item-based and Semantic Graph (Moawad & Aref, 2012) are the methods which focus primarily on the semantic representation of the source text. It is important to note that abstraction will need semantic representation at some stage. and that ATS requires two major components always -meaning extraction and summary generation in natural language.",
"cite_spans": [
{
"start": 230,
"end": 252,
"text": "(Sunitha et al., 2016)",
"ref_id": "BIBREF5"
},
{
"start": 318,
"end": 339,
"text": "(Moawad & Aref, 2012)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Major ATS approaches for Indian Languages:",
"sec_num": "3.1."
},
{
"text": "A closer look reveals that the ILs popularly use : the graph-, the POS-NER-, and textual position-based methods.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Major ATS approaches for Indian Languages:",
"sec_num": "3.1."
},
{
"text": "Of the given types, one common method is the ontology based method. Ontology refers to the 'theory of existence' or a list of all the things that exist (Russell & Norvig, 2019) . A number of such summarization tools have been developed for a 3eld-speci3c summarization. For example, Texminer is a tool that summarizes papers of Port and Coastal Engineering (Hipola et al, 2014) ; or it may be related to a particular sci-enti3c 3eld (Luhn, 1958) . We 3nd it noteworthy that ontology is important in areas where a 3nite set of vocabulary pertaining to the 3eld can be enlisted. However, in extraction techniques in NLP, a method of ontology extraction does exist (Russell & Norvig, 2019 ). This may be a possible approach to get some ontology out of a general document, but its reliability for summarization purposes may have to be tested. This brings us to the next possible approach to Indian languages text summarization which is graph-based summarization. Graphs are created out of the text document with its words as vertices and the links between them as edges (Subramaniam & Dalal, 2015) . This method can be used for languages with easy tokenization availability. An additional use of WordNet is also required here. Advanced work in graph-based methods includes 'Reduced Semantic Graph' (RSG) methods where an even more simpli3ed version of a text's graph is generated using ontology for word-sense instantiation, concept validation and sentence-ranking (Moawad & Aref, 2012) . RSG methods have been deployed for Hindi (Subramaniam & Dalal, 2015) and Malayalam (Kabeer & Idicula, 2014) . The results for Hindi are reported to be up to the mark (Subramaniam & Dalal, 2015) . Due to the rich morphology of Sanskrit, a standard word-order may not be followed even in current prose. Semantic representation thus becomes an essential element. This indicates that perhaps semantic approach would yield better results. Sakhare and Kumar (2016) . Although they use it for English through neural nets, the approach for information extraction is taken from Mimamsa which makes it relevant to our discussion. A pointer-generator method based on pre-trained word-embedding for ATS has been performed for English by Anh & Trang (2019) . The application for Sanskrit will need to be tested though they had the prepared CNN/Dailymail dataset for training already. Another e?ort in IL ATS has been by Talukder et al. (2019) where the model used is sequence to sequence RNN. They report the loss of training error to 0.008. The text-based method is classi3ed as the third method. This is the corpus-based method deployed by others (Hasler et al, 2003; Edmundson, 1969) discussed in the next section. Apart from graph-based methods, POS-NER based methods have also been deployed. Embar et al (2013) presents sArAmsha, an abstractive summarizer for Kannada. According to them, tools like POS tagging and NER implementation are used in the initial processing of documents and then an abstraction scheme is applied. This may also be classi3ed under the corpus based approach.",
"cite_spans": [
{
"start": 152,
"end": 176,
"text": "(Russell & Norvig, 2019)",
"ref_id": "BIBREF27"
},
{
"start": 357,
"end": 377,
"text": "(Hipola et al, 2014)",
"ref_id": "BIBREF13"
},
{
"start": 433,
"end": 445,
"text": "(Luhn, 1958)",
"ref_id": "BIBREF18"
},
{
"start": 662,
"end": 685,
"text": "(Russell & Norvig, 2019",
"ref_id": "BIBREF27"
},
{
"start": 1066,
"end": 1093,
"text": "(Subramaniam & Dalal, 2015)",
"ref_id": "BIBREF31"
},
{
"start": 1461,
"end": 1482,
"text": "(Moawad & Aref, 2012)",
"ref_id": "BIBREF21"
},
{
"start": 1526,
"end": 1553,
"text": "(Subramaniam & Dalal, 2015)",
"ref_id": "BIBREF31"
},
{
"start": 1568,
"end": 1592,
"text": "(Kabeer & Idicula, 2014)",
"ref_id": "BIBREF16"
},
{
"start": 1651,
"end": 1678,
"text": "(Subramaniam & Dalal, 2015)",
"ref_id": "BIBREF31"
},
{
"start": 1919,
"end": 1943,
"text": "Sakhare and Kumar (2016)",
"ref_id": "BIBREF29"
},
{
"start": 2210,
"end": 2228,
"text": "Anh & Trang (2019)",
"ref_id": "BIBREF2"
},
{
"start": 2392,
"end": 2414,
"text": "Talukder et al. (2019)",
"ref_id": "BIBREF32"
},
{
"start": 2621,
"end": 2641,
"text": "(Hasler et al, 2003;",
"ref_id": "BIBREF12"
},
{
"start": 2642,
"end": 2658,
"text": "Edmundson, 1969)",
"ref_id": "BIBREF8"
},
{
"start": 2769,
"end": 2787,
"text": "Embar et al (2013)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Major ATS approaches for Indian Languages:",
"sec_num": "3.1."
},
{
"text": "Under this, Corpus is annotated with relevant annotation schemes like POS, NER, discourse annotation tools like the Rhetorical-Structure Theory (Mann & Thompson, 1988; Jones, 1999; Zahri et al., 2015) etc, which helps in extracting meaning at a later stage. Corpus type has also been used as an important basis for developing TS (Hasler et al., 2003) . Annotation of corpora to indicate meaningful units in a text is a viable method. The works suggest that semantic abstraction becomes easier with this annotated corpora. However, Oya T. et al. (2014) use template-based abstractive summarization which they report has reduced dependence on annotated corpora.",
"cite_spans": [
{
"start": 144,
"end": 167,
"text": "(Mann & Thompson, 1988;",
"ref_id": "BIBREF20"
},
{
"start": 168,
"end": 180,
"text": "Jones, 1999;",
"ref_id": "BIBREF14"
},
{
"start": 181,
"end": 200,
"text": "Zahri et al., 2015)",
"ref_id": "BIBREF33"
},
{
"start": 329,
"end": 350,
"text": "(Hasler et al., 2003)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus based approach:",
"sec_num": "3.1.3."
},
{
"text": "At this point, it is important to mention the extraction-based abstraction approach to TS one of which is the Information Extraction(IE) ATS (Kallimani et al, 2011). IE techniques are deployed in the initial stages in order to identify the important word units in the document. Abstraction is done from these extracted units (Kallimani et al, 2011; Afantenos et al., 2005) . Edmundson(1969) used a proper corpus divided into training and testing for summarization and evaluation. The method used is feature based only and he suggested that it was important to consider syntactic and semantic features in summarization. It may be noted that the 'abstracting' referred to in his article is focused on generating abstracts of articles based on extracted sentences. He terms this process as 'abstracting' (Edmundson & Wylls, 1961) , though it is di?erent from abstraction as we know it today. Other than ATS, some prominent works in ETS for Indian Languages have been covered by Dhanya & Jathavedan (2013) . The latter includes the thematic and positional score based method for Bengali (Sarkar, 2012) ; statistical features like cue phrase, title keyword, and similar features based extraction method for Punjabi (Gupta & Lehal, 2011) ; the graph-based text ranking method for Tamil (Sankar et al, 2011) performs extractive summary without any annotated corpora or supervised learning method. Patel et al. (2007) and D'Silva & Sharma (2019) look at multilingual translation problems with language independent TS being one option (Patel et al., 2007) . There are two reasons why it may not be useful to us. First, their approach is statistical and not semantic. It has been suggested by Edmundson (1969) that syntactic and semantic factors as well as context of a text (Jones, 1999) in TS be considered for better quality. We too believe that semantic representation is important for ATS. Two, their approach is mostly extractive. The other option, that of cross lingual TS using Machine Translation (MT) (D'Silva & Sharma, 2019) is a good option to be explored.",
"cite_spans": [
{
"start": 325,
"end": 348,
"text": "(Kallimani et al, 2011;",
"ref_id": "BIBREF15"
},
{
"start": 349,
"end": 372,
"text": "Afantenos et al., 2005)",
"ref_id": "BIBREF1"
},
{
"start": 375,
"end": 390,
"text": "Edmundson(1969)",
"ref_id": "BIBREF8"
},
{
"start": 801,
"end": 826,
"text": "(Edmundson & Wylls, 1961)",
"ref_id": null
},
{
"start": 975,
"end": 1001,
"text": "Dhanya & Jathavedan (2013)",
"ref_id": "BIBREF25"
},
{
"start": 1083,
"end": 1097,
"text": "(Sarkar, 2012)",
"ref_id": "BIBREF30"
},
{
"start": 1210,
"end": 1231,
"text": "(Gupta & Lehal, 2011)",
"ref_id": "BIBREF10"
},
{
"start": 1280,
"end": 1300,
"text": "(Sankar et al, 2011)",
"ref_id": "BIBREF28"
},
{
"start": 1390,
"end": 1409,
"text": "Patel et al. (2007)",
"ref_id": "BIBREF24"
},
{
"start": 1526,
"end": 1546,
"text": "(Patel et al., 2007)",
"ref_id": "BIBREF24"
},
{
"start": 1683,
"end": 1699,
"text": "Edmundson (1969)",
"ref_id": "BIBREF8"
},
{
"start": 1765,
"end": 1778,
"text": "(Jones, 1999)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "3.1.4.",
"sec_num": null
},
{
"text": "A key point to be observed in these and general text summarization tools is the type and source of data. There are two primary domains of data on which most tools are based: Scienti3c articles and newspaper articles. Tools for the summary of these two types of texts are usually developed more. While extractive is a dominant approach for these domains, abstractive has also a good presence. However, to begin a process in Sanskrit ATS, we have focused our study on contemporary prose consisting of mainly newspaper articles and Sanskrit blogs. Observations regarding methods:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "3.1.5.",
"sec_num": null
},
{
"text": "1. Scholars use TS methods in a mixed manner. For e.g., a semantic graph may require ontology deployment for better semantic representation (Moawad & Aref, 2012) ; abstractive summarizer may 3rst extract relevant information before applying abstraction (Kalimanni et al, 2011).",
"cite_spans": [
{
"start": 140,
"end": 161,
"text": "(Moawad & Aref, 2012)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "3.1.5.",
"sec_num": null
},
{
"text": "2. Supervised methods will need label summaries along with the texts. Thus, newspaper articles with their headlines are usually taken as the standard training corpus where the headline serves as the summary of the respective text. This is a feasible approach for a beginner-level work.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "3.1.5.",
"sec_num": null
},
{
"text": "Some features of the Sanskrit writings and their challenges can be stated as following:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Sanskrit ATS",
"sec_num": "4."
},
{
"text": "\u2022 Sanskrit prose is strictly based on the principles of grammar which inspires its word-formation and usage. Owing to the Paninian model of Grammar, the language is rich in morphology. The principle of economy and precision have been important for Sanskrit prose (Kiparsky, 1991) . As a result, while the prose in Sanskrit in general is appreciated for its economy, it becomes di6cult for any man/machine processing, and more so for the ATS.",
"cite_spans": [
{
"start": 263,
"end": 279,
"text": "(Kiparsky, 1991)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Sanskrit ATS",
"sec_num": "4."
},
{
"text": "\u2022 Compounds and Sandhis: Sanskrit prose is constituted on the samhita (continuous text) principle thereby using Compounds and Sandhis (euphonic combination) heavily. For instance, multiple words combined after removing their inOections is an example of a compound. Space does not act as a delimiter largely here. This along with potentially recursive sandhi and complex morphology make preprocessing a critical task for Sanskrit texts.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Sanskrit ATS",
"sec_num": "4."
},
{
"text": "\u2022 Word Signicance: Most Sanskrit literary works, especially poetry, tend to be indirect in their intended meanings -abhidha(literal), lakshana(metaphor), vyanjana(euphemism). Poetry usually expresses meanings more than one but the same can go for most prose creations in literature also. The availability of lexical resources like the Amarakosha bear testimony to this fact, so does the long tradition of language analysis including the philosophy of Mimamsa (interpretation) and Nyaya (logic).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Sanskrit ATS",
"sec_num": "4."
},
{
"text": "\u2022 Diversity of verb usage: While lakaras (tense) are used to denote time, some su6xes are also used to indicate past and present tense. Thus, for the same verb, di?erent forms of it can be used to suggest the same meaning. For each such usage, meaning will have to be considered well before generating a summary of any type.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Sanskrit ATS",
"sec_num": "4."
},
{
"text": "To perform a preliminary data study, a total of 1310 sentences have been extracted from online sources and stored as data 3les. Current prose like the news articles from the All India Radio, DD News and other sources have been considered at this stage. The following may be observed about the data:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Preliminary Study",
"sec_num": "5."
},
{
"text": "1. Sentences are usually short, with not more than 7 words per sentence on an average.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Preliminary Study",
"sec_num": "5."
},
{
"text": "2. Owing to the fact that most digital sources in Sanskrit found so far exist as a way to teach prospective learners, there is no variety in content found there.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Preliminary Study",
"sec_num": "5."
},
{
"text": "3. News articles o?er a good standard of sentences in Sanskrit while at the same time reducing the complexity of verbs. There are a few standard usages which ensure ease of meaning comprehension.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Preliminary Study",
"sec_num": "5."
},
{
"text": "The short length of sentences indicates that with some basic preprocessing only, a TS method may be applied on the text. After going through the preliminary data, this has led us to conclude that we may start our work with focus on two approaches: 3rst, a graphbased method. Owing to short sentences in the current prose, generating a graph and the prospective relations among words may be quicker and e6cient. Second, supervised method where news articles and their headlines are taken as corpora for training. This would be on the lines of the ATS developed on English and other languages using the CNN/Dailymail dataset (Mishra & Gayen, 2018) . Preprocessing of the text is a necessary stage in the approach (Barve et. al, 2015) . This would ensure creation of words for ease of processing the text further. Contemporary simple prose that contains direct meanings instead of oblique ones should be used like Barve et al (2015) use Sanskrit Wikipedia articles to test their approaches (VSM, Graph and tf-isf). A work on these two methods will suggest further course of action. Annotation may be required if the results so indicate.",
"cite_spans": [
{
"start": 623,
"end": 645,
"text": "(Mishra & Gayen, 2018)",
"ref_id": "BIBREF22"
},
{
"start": 711,
"end": 731,
"text": "(Barve et. al, 2015)",
"ref_id": "BIBREF3"
},
{
"start": 911,
"end": 929,
"text": "Barve et al (2015)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Preliminary Study",
"sec_num": "5."
},
{
"text": "This paper presents a preliminary attempt to develop a Sanskrit abstractive text summarizer for current prose. It surveyed the top abstractive summarization approaches to Indian languages, in general, with a view to zeroing in on one approach for the current work on Sanskrit ATS. Since there has not been any attempt at Sanskrit ATS so far, a beginning is being made for current Sanskrit prose mostly news articles. While summarization would not suit literary poetry, we could utilize dependency parsers to build semantic graphs for any verse in scienti3c texts. Prose in these texts could be further summarized if this work is advanced further from current prose to other prose styles. After surveying the available literature for ATS in ILs the authors propose that semantic approach would be better suited for the inherent complexities that Sanskrit is known for. Owing to rich morphology of the language, pre-de3ned structures may not result in a coherent or usable summary. Thus, a semantic approach would assist in arriving at a better analyzed summary. In the semantic approach, a graph-based method shall be a good start. Secondly, a supervised method for the available prose from the news article-headline combine may be emulated for Sanskrit too. The possibility of annotation should be considered after this, if required. The language of the output summary is one dimension of SATS which is out of the scope of this paper. For any other language, the abstracted summary is produced in the same language as the text. However, it",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6."
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "be explored if the abstractions of Sanskrit prose could be carried out in both Sanskrit as well as Hindi or English with the help of an existing Machine Translation",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "be explored if the abstractions of Sanskrit prose could be carried out in both Sanskrit as well as Hindi or English with the help of an existing Machine Trans- lation.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Summarization from Medical Documents: A Survey. Artificial Intelligence in Medicine",
"authors": [
{
"first": "S",
"middle": [],
"last": "Afantenos",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Karkaletsis",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Stamatopoulos",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"33.157-177.10.1016/j.artmed.2004.07.017"
]
},
"num": null,
"urls": [],
"raw_text": "Afantenos, S., Karkaletsis, V. & Stamatopoulos, P. (2005). Summarization from Medical Documents: A Survey. Artificial Intelligence in Medicine. 33. 157-177. 10.1016/j.artmed.2004.07.017.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Abstractive Text Summarization Using Pointer-Generator Networks With Pre-trained Word Embedding",
"authors": [
{
"first": "D",
"middle": [
"T"
],
"last": "Anh",
"suffix": ""
},
{
"first": "N",
"middle": [
"T T"
],
"last": "Trang",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the Tenth International Symposium on Information and Communication Technology",
"volume": "",
"issue": "",
"pages": "473--478",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anh, D. T., & Trang, N. T. T. (2019, December). Abstractive Text Summarization Using Pointer-Generator Networks With Pre-trained Word Embedding. In Proceedings of the Tenth International Symposium on Information and Communication Technology (pp. 473-478).",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Query-Based Extractive Text Summarization for Sanskrit",
"authors": [
{
"first": "S",
"middle": [],
"last": "Barve",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Desai",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Sardinha",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the Fourth International Conference on Frontiers in Intelligent Computing: Theory and Applications(FICTA)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Barve, S, Desai, S. & Sardinha, R. (2015). \"Query-Based Extractive Text Summarization for Sanskrit\". In: Proceedings of the Fourth International Conference on Frontiers in Intelligent Computing: Theory and Applications(FICTA) .",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Digital Object",
"authors": [
{
"first": "",
"middle": [],
"last": "Springer",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"I:10.1007/978-81-322-2695-6_47"
]
},
"num": null,
"urls": [],
"raw_text": "Springer. Digital Object I: 10.1007/978-81-322-2695-6_47",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "A Study on Abstractive Summarization Techniques in Indian Languages",
"authors": [
{
"first": "C",
"middle": [],
"last": "Sunitha",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Jaya",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Ganesh",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the Fourth International Conference on Recent Trends in Computer Science and Engineering",
"volume": "87",
"issue": "",
"pages": "25--31",
"other_ids": {
"DOI": [
"10.1016/j.procs.2016.05.121"
]
},
"num": null,
"urls": [],
"raw_text": "C. Sunitha, A., Jaya, & Ganesh, A. (2016). \"A Study on Abstractive Summarization Techniques in Indian Languages\". In: Proceedings of the Fourth International Conference on Recent Trends in Computer Science and Engineering. Procedia Computer Science. 87(2016). pp 25-31. Elsevier: DOI: 10.1016/j.procs.2016.05.121",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Automatic Text Summarization of Indian Languages: A Multilingual Problem",
"authors": [
{
"first": "J",
"middle": [],
"last": "D'silva",
"suffix": ""
},
{
"first": "U",
"middle": [],
"last": "Sharma",
"suffix": ""
}
],
"year": 2019,
"venue": "Journal of Theoretical and Applied Information Technology",
"volume": "97",
"issue": "11",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "D'Silva, J. & Sharma, U (2019). Automatic Text Summarization of Indian Languages: A Multilingual Problem. Journal of Theoretical and Applied Information Technology. 97(11).",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "sArAmsha -A Kannada abstractive summarizer",
"authors": [
{
"first": "V",
"middle": [],
"last": "Embar",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Deshpande",
"suffix": ""
},
{
"first": "A",
"middle": [
"K"
],
"last": "Vaishnavi",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Jain",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Kallimani",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"ICACCI2013.540-544.10.1109/ICACCI.2013.6637229"
]
},
"num": null,
"urls": [],
"raw_text": "Embar, V., Deshpande, S., Vaishnavi, A.K. & Jain, V. & Kallimani, J. (2013). sArAmsha -A Kannada abstractive summarizer. In: Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics , ICACCI 2013. 540-544. 10.1109/ICACCI.2013.6637229.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "New methods in automatic extracting",
"authors": [
{
"first": "H",
"middle": [
"P"
],
"last": "Edmundson",
"suffix": ""
}
],
"year": 1969,
"venue": "Journal of the ACM (JACM)",
"volume": "16",
"issue": "2",
"pages": "264--285",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the ACM (JACM) , 16 (2), 264-285.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Automatic abstracting and indexing-survey and recommendations",
"authors": [
{
"first": "H",
"middle": [
"P"
],
"last": "Edmundson",
"suffix": ""
},
{
"first": "R",
"middle": [
"E"
],
"last": "Wyllys",
"suffix": ""
}
],
"year": 1961,
"venue": "Communications of the ACM",
"volume": "4",
"issue": "5",
"pages": "226--234",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Edmundson, H. P., & Wyllys, R. E. (1961). Automatic abstracting and indexing-survey and recommendations. Communications of the ACM , 4 (5), 226-234.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Features Selection and Weight learning for Punjabi Text Summarization",
"authors": [
{
"first": "V",
"middle": [],
"last": "Gupta",
"suffix": ""
},
{
"first": "G",
"middle": [
"S"
],
"last": "Lehal",
"suffix": ""
}
],
"year": 2011,
"venue": "International Journal of Engineering Trends and Technology",
"volume": "2",
"issue": "2",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gupta, V., & Lehal, G.S. (2011). Features Selection and Weight learning for Punjabi Text Summarization. International Journal of Engineering Trends and Technology. 2(2).",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Extractive vs. NLG-based Abstractive Summarization of Evaluative Text: The Effect of Corpus Controversiality",
"authors": [
{
"first": "C",
"middle": [
"K"
],
"last": "Giuseppe C & Jackie",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the Fifth International Natural Language Generation Conference, ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Giuseppe C & Jackie C. K. (2008), Extractive vs. NLG-based Abstractive Summarization of Evaluative Text: The Effect of Corpus Controversiality , Proceedings of the Fifth International Natural Language Generation Conference, ACL, https://www.aclweb.org/anthology/W08-1106",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Building better corpora for summarization",
"authors": [
{
"first": "L",
"middle": [],
"last": "Hasler",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Orasan",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Mitkov",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of Corpus Linguistics",
"volume": "",
"issue": "",
"pages": "309--319",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hasler, L., Orasan, C., & Mitkov, R. (2003). Building better corpora for summarization. In Proceedings of Corpus Linguistics (pp. 309-319).",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Ontology-based text summarization. The case of Texminer",
"authors": [
{
"first": "P",
"middle": [],
"last": "Hipola",
"suffix": ""
},
{
"first": "J",
"middle": [
"A"
],
"last": "Senso",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Mederos-Leiva",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Dominguez-Velasco",
"suffix": ""
}
],
"year": 2014,
"venue": "Library HiTech",
"volume": "32",
"issue": "2",
"pages": "229--248",
"other_ids": {
"DOI": [
"10.1108/LHT-01-2014-0005"
]
},
"num": null,
"urls": [],
"raw_text": "Hipola, P., Senso, J.A., Mederos-Leiva, A. & Dominguez-Velasco, S. (2014). Ontology-based text summarization. The case of Texminer. Library HiTech. 32(2) . pp 229-248. Emerald. DOI: 10.1108/LHT-01-2014-0005.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Automatic summarizing: factors and directions",
"authors": [
{
"first": "K",
"middle": [
"S"
],
"last": "Jones",
"suffix": ""
}
],
"year": 1999,
"venue": "Advances in automatic text summarization",
"volume": "",
"issue": "",
"pages": "1--12",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jones, K. S. (1999). Automatic summarizing: factors and directions. In Mani & Maybury (eds.) Advances in automatic text summarization (No. 1, pp. 1-12).",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Information extraction by an abstractive text summarization for an Indian regional language",
"authors": [
{
"first": "J",
"middle": [
"S"
],
"last": "Kallimani",
"suffix": ""
},
{
"first": "K",
"middle": [
"G"
],
"last": "Srinivasa",
"suffix": ""
}
],
"year": 2011,
"venue": "2011 7th International Conference on Natural Language Processing and Knowledge Engineering",
"volume": "",
"issue": "",
"pages": "319--322",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kallimani, J. S., & Srinivasa, K. G. (2011). Information extraction by an abstractive text summarization for an Indian regional language. In 2011 7th International Conference on Natural Language Processing and Knowledge Engineering (pp. 319-322). IEEE.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Text summarization for Malayalam documents -An experience",
"authors": [
{
"first": "R",
"middle": [],
"last": "Kabeer",
"suffix": ""
},
{
"first": "S",
"middle": [
"M"
],
"last": "Idicula",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the International Conference on Data Science & Engineering (ICDSE)",
"volume": "",
"issue": "",
"pages": "145--150",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kabeer, R. & Idicula, S. M.(2014). \"Text summarization for Malayalam documents -An experience\" In: Proceedings of the International Conference on Data Science & Engineering (ICDSE), Kochi, pp. 145-150.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Economy and the Construction of Sivasutras",
"authors": [
{
"first": "P",
"middle": [],
"last": "Kiparsky",
"suffix": ""
}
],
"year": 1991,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kiparsky, P. (1991). Economy and the Construction of Sivasutras. PDF.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "The automatic creation of literature abstracts",
"authors": [
{
"first": "H",
"middle": [
"P"
],
"last": "Luhn",
"suffix": ""
}
],
"year": 1958,
"venue": "IBM Journal of research and development",
"volume": "2",
"issue": "2",
"pages": "159--165",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of research and development , 2 (2), 159-165. .",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Advances in Automatic Summarization",
"authors": [
{
"first": "I",
"middle": [],
"last": "Mani",
"suffix": ""
},
{
"first": "M",
"middle": [
"T"
],
"last": "Maybury",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mani, I. & Maybury, M. T. (1999). Advances in Automatic Summarization. MIT Press.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Rhetorical structure theory: Toward a functional theory of text organization",
"authors": [
{
"first": "W",
"middle": [
"C"
],
"last": "Mann",
"suffix": ""
},
{
"first": "S",
"middle": [
"A"
],
"last": "Thompson",
"suffix": ""
}
],
"year": 1988,
"venue": "Text-interdisciplinary Journal for the Study of Discourse",
"volume": "8",
"issue": "3",
"pages": "243--281",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mann, W. C., & Thompson, S. A. (1988). Rhetorical structure theory: Toward a functional theory of text organization. Text-interdisciplinary Journal for the Study of Discourse , 8 (3). pp. 243-281.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Semantic Graph Reduction Approach for Abstractive Text Summarization",
"authors": [
{
"first": "",
"middle": [],
"last": "Moawad",
"suffix": ""
},
{
"first": "",
"middle": [
"M"
],
"last": "Aref",
"suffix": ""
}
],
"year": 2012,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.1109/ICCES.2012.6408498"
]
},
"num": null,
"urls": [],
"raw_text": "Moawad, I F & Aref. M. ( 2012). \"Semantic Graph Reduction Approach for Abstractive Text Summarization\". In: ICCES. p 132-138. DOI: 10.1109/ICCES.2012.6408498",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Automatic Lossless Summarization of News Articles with Abstract Meaning Representation",
"authors": [
{
"first": "R",
"middle": [],
"last": "Mishra",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Gayen",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 3rd International Conference",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mishra, R. and Gayen, T. (2018). \"Automatic Lossless Summarization of News Articles with Abstract Meaning Representation.\" In: Proceedings of the 3rd International Conference Computer Science and Computational Engineering. Procedia Computer Science. PDF.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "A template-based abstractive meeting summarization: Leveraging summary and source text relationships",
"authors": [
{
"first": "T",
"middle": [],
"last": "Oya",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Mehdad",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Carenini",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Ng",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 8th International Natural Language Generation Conference (INLG)",
"volume": "",
"issue": "",
"pages": "45--53",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Oya, T., Mehdad, Y., Carenini, G., & Ng, R. (2014). A template-based abstractive meeting summarization: Leveraging summary and source text relationships. In Proceedings of the 8th International Natural Language Generation Conference (INLG) : pp. 45-53.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "A language independent approach to multilingual text summarization. Large scale semantic access to content (text, image, video, and sound",
"authors": [
{
"first": "A",
"middle": [],
"last": "Patel",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Siddiqui",
"suffix": ""
},
{
"first": "U",
"middle": [
"S"
],
"last": "Tiwary",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "",
"issue": "",
"pages": "123--132",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Patel, A., Siddiqui, T., & Tiwary, U. S. (2007). A language independent approach to multilingual text summarization. Large scale semantic access to content (text, image, video, and sound) , 123-132.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Comparative Study of Text Summarization in Indian Languages",
"authors": [
{
"first": "P",
"middle": [
"M"
],
"last": "Dhanya",
"suffix": ""
},
{
"first": "&",
"middle": [],
"last": "Jathavedan",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "",
"suffix": ""
}
],
"year": 2013,
"venue": "International Journal of Computer Applications",
"volume": "75",
"issue": "6",
"pages": "17--21",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "P.M, Dhanya & Jathavedan M. (2013). \"Comparative Study of Text Summarization in Indian Languages.\" In: International Journal of Computer Applications. 75(6) : pp 17-21.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Ontology-Based Automatic Text Summarization using FarsNet",
"authors": [
{
"first": "M",
"middle": [],
"last": "Ramezani",
"suffix": ""
},
{
"first": "Md",
"middle": [
"R"
],
"last": "Feizi-Derakhshi",
"suffix": ""
}
],
"year": 2015,
"venue": "Advances in Computer Science: an International Journal",
"volume": "4",
"issue": "2",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ramezani, M. & Feizi-Derakhshi, Md. R. (2015). Ontology-Based Automatic Text Summarization using FarsNet. Advances in Computer Science: an International Journal. 4(2) no.14.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Artificial Intelligence: A Modern Approach",
"authors": [
{
"first": "S",
"middle": [
"J"
],
"last": "Russell",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Norvig",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Russell, S J. & Norvig, P. (2019). Artificial Intelligence: A Modern Approach. Pearson.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Text Extraction for an Agglutinative Language",
"authors": [
{
"first": "K",
"middle": [],
"last": "Sankar",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "",
"suffix": ""
},
{
"first": "Vijay",
"middle": [
"Sundar"
],
"last": "Kumar",
"suffix": ""
},
{
"first": "Devi",
"middle": [],
"last": "",
"suffix": ""
},
{
"first": "S",
"middle": [
"L"
],
"last": "",
"suffix": ""
}
],
"year": 2011,
"venue": "Problem of Parsing in Indian languages",
"volume": "11",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sankar, K., R, Vijay Sundar Kumar, Devi, S.L. (2011). Text Extraction for an Agglutinative Language. Language in India. 11(5). Special Vol: Problem of Parsing in Indian languages.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Syntactical Knowledge and Sanskrit Memansa Principle Based Approach for Text Summarization",
"authors": [
{
"first": "D",
"middle": [
"Y"
],
"last": "Sakhare",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Kumar",
"suffix": ""
}
],
"year": 2016,
"venue": "In: International Journal of Computer Science and Information Security (IJCSIS)",
"volume": "14",
"issue": "4",
"pages": "1947--5500",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sakhare, D.Y. and Kumar R (2016). Syntactical Knowledge and Sanskrit Memansa Principle Based Approach for Text Summarization\" In: International Journal of Computer Science and Information Security (IJCSIS). 14(4). pp. 270-275. ISSN: 1947-5500.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Bengali text summarization by sentence extraction",
"authors": [
{
"first": "K",
"middle": [],
"last": "Sarkar",
"suffix": ""
}
],
"year": 2012,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1201.2240"
]
},
"num": null,
"urls": [],
"raw_text": "Sarkar, K. (2012). Bengali text summarization by sentence extraction. arXiv preprint arXiv:1201.2240 .",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Test Model for Rich Semantic Graph Representation for Hindi Text using Abstractive Method",
"authors": [
{
"first": "M",
"middle": [],
"last": "Subramaniam",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Dalal",
"suffix": ""
}
],
"year": 2015,
"venue": "In: International Research Journal of Engineering and Technology",
"volume": "2",
"issue": "2",
"pages": "2395--0056",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Subramaniam, M. & Dalal V. (2015). \"Test Model for Rich Semantic Graph Representation for Hindi Text using Abstractive Method\" In: International Research Journal of Engineering and Technology. 2(2). pp 113-116. e-ISSN:2395-0056",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Bengali abstractive text summarization using sequence to sequence RNNs",
"authors": [
{
"first": "M",
"middle": [
"A I"
],
"last": "Talukder",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Abujar",
"suffix": ""
},
{
"first": "A",
"middle": [
"K M"
],
"last": "Masum",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Faisal",
"suffix": ""
},
{
"first": "S",
"middle": [
"A"
],
"last": "Hossain",
"suffix": ""
}
],
"year": 2019,
"venue": "10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)",
"volume": "",
"issue": "",
"pages": "1--5",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Talukder, M. A. I., Abujar S., Masum, A. K. M., Faisal, F. & Hossain, S. A. (2019). \"Bengali abstractive text summarization using sequence to sequence RNNs,\" 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) . pp. 1-5.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Applications of Rhetorical Relations Between Sentences to Cluster-Based Text Summarization",
"authors": [
{
"first": "N",
"middle": [
"A H"
],
"last": "Zahri",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Fukumoto",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Suguru",
"suffix": ""
},
{
"first": "O",
"middle": [
"B"
],
"last": "Lynn",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ccsea",
"suffix": ""
},
{
"first": "Aifu",
"middle": [],
"last": "Dkmp",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sea-",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "",
"issue": "",
"pages": "73--92",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zahri, N.A.H., Fukumoto, F., Suguru, M., & Lynn, O.B. (2015). Applications of Rhetorical Relations Between Sentences to Cluster-Based Text Summarization. in Nagamalai et al. (eds.) CCSEA, DKMP, AIFU, SEA-2015. pp. 73-92.",
"links": null
}
},
"ref_entries": {
"TABREF0": {
"content": "<table><tr><td>help of a semantic analysis technique (Maji & Fizi-</td></tr><tr><td>Derakhshi, 2015).</td></tr><tr><td>A neural-network based application of the Memansa</td></tr><tr><td>principle is used by</td></tr></table>",
"text": "Unsupervised methods include graph-based, VSM, text-based. Graph-based method can be grouped with the semantic graph approach mentioned earlier. It creates a graph with concepts as vertices and the relation between them as edges(Majod, & Fizi-Derakhshi, 2015). VSM technique creates vectors of the units of text and then the most important units are extracted with the",
"type_str": "table",
"num": null,
"html": null
}
}
}
} |