Data Card.

README.md

@@ -222,19 +222,19 @@ Each instance in the dataset has five fields.
 
 4. "teams": This filed is a dictionary of multiple nested dictionaries. On the highest level, it has two keys: 'home' and 'vis', which provide the stats for home team and visiting team of the game. Both are dictionaries with same structure. Each dictionary will contain team's information such as name of the team, their total wins/losses in current season, their conference standing, the SportSett ids for their current and previous games. Apart from these general information, they also have the box- and line- scores for the team in the game. Box score is the stats of players from the team at the end of the game, while line score along with the whole game stats is divided into quarters and halves as well as the extra-time (if happened in the game). After these scores, there is another field of next-game, which gives general information about team's next game such as the place and opponent's name of the next game.
 
-5. "summaries": This is a list of summaries for each game. Some games will have more than one summary, in that case, the list will have more than one entries. Each summary in the list is a string which can be tokenised by a space, following the practices in RotoWire-FG dataset ((Wang, 2019)[https://www.aclweb.org/anthology/W19-8639]).
+5. "summaries": This is a list of summaries for each game. Some games will have more than one summary, in that case, the list will have more than one entries. Each summary in the list is a string which can be tokenised by a space, following the practices in RotoWire-FG dataset ([Wang, 2019](https://www.aclweb.org/anthology/W19-8639)).
 
 #### Reason for Structure
 
 <!-- info: How was the dataset structure determined? -->
 <!-- scope: microscope -->
-The structure mostly follows the original structure defined in RotoWire dataset ((Wiseman et. al. 2017)[https://aclanthology.org/D17-1239/]) with some modifications (such as game and next-game keys) address the problem of information gap between input and output data ((Thomson et. al. 2020)[https://aclanthology.org/2020.inlg-1.6/]). 
+The structure mostly follows the original structure defined in RotoWire dataset ([Wiseman et. al. 2017](https://aclanthology.org/D17-1239/)) with some modifications (such as game and next-game keys) address the problem of information gap between input and output data ([Thomson et. al. 2020](https://aclanthology.org/2020.inlg-1.6/)).
 
 #### How were labels chosen?
 
 <!-- info: How were the labels chosen? -->
 <!-- scope: microscope -->
-Similar to RotoWire dataset ((Wiseman et. al. 2017)[https://aclanthology.org/D17-1239/]) 
+Similar to RotoWire dataset ([Wiseman et. al. 2017](https://aclanthology.org/D17-1239/))
 
 #### Example Instance
 
@@ -1340,7 +1340,7 @@ The splits were created as per different NBA seasons. All the games in regular s
 
 <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
 <!-- scope: microscope -->
-This dataset contains a data analytics problem in the classic sense ((Reiter, 2007)[https://aclanthology.org/W07-2315]).  That is, there is a large amount of data from which insights need to be selected.  Further, the insights should be both from simple shallow queries (such as dirext transcriptions of the properties of a subject, i.e., a player and their statistics), as well as aggregated (how a player has done over time).  There is far more on the data side than is required to be realised, and indeed, could be practically realised.  This depth of data analytics problem does not exist in other datasets.
+This dataset contains a data analytics problem in the classic sense ([Reiter, 2007](https://aclanthology.org/W07-2315)).  That is, there is a large amount of data from which insights need to be selected.  Further, the insights should be both from simple shallow queries (such as dirext transcriptions of the properties of a subject, i.e., a player and their statistics), as well as aggregated (how a player has done over time).  There is far more on the data side than is required to be realised, and indeed, could be practically realised.  This depth of data analytics problem does not exist in other datasets.
 
 #### Similar Datasets
 
@@ -1376,20 +1376,18 @@ no
 
 <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
 <!-- scope: microscope -->
-For dataset discussion see:
-Thomson et al, 2020 https://aclanthology.org/2020.intellang-1.4/
+For dataset discussion see [Thomson et al, 2020](https://aclanthology.org/2020.intellang-1.4/)
 
 For evaluation see:
-Thomson & Reiter 2020
-Thomson & Reiter (2021): https://aclanthology.org/2021.inlg-1.23
-Kasner et al (2021): https://aclanthology.org/2021.inlg-1.25
+- [Thomson & Reiter 2020, Thomson & Reiter (2021)](https://aclanthology.org/2021.inlg-1.23)
+- [Kasner et al (2021)](https://aclanthology.org/2021.inlg-1.25)
 
 For a system using the relational database form of SportSett, see:
-Thomson et al (2020): https://aclanthology.org/2020.inlg-1.6/
+- [Thomson et al (2020)](https://aclanthology.org/2020.inlg-1.6/)
 
 For recent systems using the Rotowire dataset, see:
-Puduppully & Lapata (2021): https://github.com/ratishsp/data2text-macro-plan-py
-Rebuffel et all (2020): https://github.com/KaijuML/data-to-text-hierarchical
+- [Puduppully & Lapata (2021)](https://github.com/ratishsp/data2text-macro-plan-py)
+- [Rebuffel et all (2020)](https://github.com/KaijuML/data-to-text-hierarchical)
 
 
 
@@ -1413,7 +1411,7 @@ Many, if not all aspects of data-to-text systems can be measured with this datas
 
 <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
 <!-- scope: microscope -->
-BLEU is the only off-the-shelf metric commonly used.  Works have also used custom metrics like RG (Wiseman et al, 2017: https://aclanthology.org/D17-1239), and a recent shared task explored other metrics and their corrolation with human evaluation ((Thomson & Reiter, 2021)[https://aclanthology.org/2021.inlg-1.23]).
+BLEU is the only off-the-shelf metric commonly used.  Works have also used custom metrics like RG ([Wiseman et al, 2017](https://aclanthology.org/D17-1239)), and a recent shared task explored other metrics and their corrolation with human evaluation ([Thomson & Reiter, 2021](https://aclanthology.org/2021.inlg-1.23)).
 
 #### Previous results available?
 
@@ -1425,8 +1423,7 @@ yes
 
 <!-- info: What evaluation approaches have others used? -->
 <!-- scope: periscope -->
-Most results from prior works use the original Rotowire dataset, which has train/validation/test contamination.  For results of BLEU and RG on the relational database format of SportSett, as a guide, see Thomson et al, 2020: 
- https://aclanthology.org/2020.inlg-1.6.
+Most results from prior works use the original Rotowire dataset, which has train/validation/test contamination.  For results of BLEU and RG on the relational database format of SportSett, as a guide, see [Thomson et al, 2020](https://aclanthology.org/2020.inlg-1.6).
 
 #### Relevant Previous Results
 
@@ -1444,13 +1441,13 @@ The results on this dataset are largely unexplored, as is the selection of suita
 
 <!-- info: Original curation rationale -->
 <!-- scope: telescope -->
-The references texts were taken from the existing dataset RotoWire-FG (Wang, 2019: https://www.aclweb.org/anthology/W19-8639), which is in turn based on Rotowire (Wiseman et al, 2017: https://aclanthology.org/D17-1239).  The rationale behind this dataset was to re-structure the data such that aggregate statistics over multiple games, as well as upcoming game schedules could be included, moving the dataset from snapshots of single games, to a format where almost everything that could be present in the reference texts could be found in the data.
+The references texts were taken from the existing dataset RotoWire-FG ([Wang, 2019](https://www.aclweb.org/anthology/W19-8639)), which is in turn based on Rotowire ([Wiseman et al, 2017](https://aclanthology.org/D17-1239)).  The rationale behind this dataset was to re-structure the data such that aggregate statistics over multiple games, as well as upcoming game schedules could be included, moving the dataset from snapshots of single games, to a format where almost everything that could be present in the reference texts could be found in the data.
 
 #### Communicative Goal
 
 <!-- info: What was the communicative goal? -->
 <!-- scope: periscope -->
-Create a summary of a basketball, with insightful facts about the game, teams, and players, both within the game, withing periods during the game, and over the course of seasons/careers where appropriate.  This is a data-to-text problem in the classic sense (Reiter, 2007: https://aclanthology.org/W07-2315) in that it has a difficult data analystics state, in addition to ordering and transcription of selected facts.
+Create a summary of a basketball, with insightful facts about the game, teams, and players, both within the game, withing periods during the game, and over the course of seasons/careers where appropriate.  This is a data-to-text problem in the classic sense ([Reiter, 2007](https://aclanthology.org/W07-2315)) in that it has a difficult data analystics state, in addition to ordering and transcription of selected facts.
 
 #### Sourced from Different Sources
 

sportsett_basketball.json

@@ -3,12 +3,12 @@
     "results": {
       "other-metrics-definitions": "N/A",
       "has-previous-results": "yes",
-      "current-evaluation": "Most results from prior works use the original Rotowire dataset, which has train/validation/test contamination.  For results of BLEU and RG on the relational database format of SportSett, as a guide, see Thomson et al, 2020: \n https://aclanthology.org/2020.inlg-1.6.",
+      "current-evaluation": "Most results from prior works use the original Rotowire dataset, which has train/validation/test contamination.  For results of BLEU and RG on the relational database format of SportSett, as a guide, see [Thomson et al, 2020](https://aclanthology.org/2020.inlg-1.6).",
       "previous-results": "The results on this dataset are largely unexplored, as is the selection of suitable metrics that correlate with human judgment.  See Thomson et al, 2021 (https://aclanthology.org/2021.inlg-1.23) for an overview, and  Kasner et al (2021) for the best performing metric at the time of writing (https://aclanthology.org/2021.inlg-1.25).",
       "metrics": [
         "BLEU"
       ],
-      "original-evaluation": "BLEU is the only off-the-shelf metric commonly used.  Works have also used custom metrics like RG (Wiseman et al, 2017: https://aclanthology.org/D17-1239), and a recent shared task explored other metrics and their corrolation with human evaluation ((Thomson & Reiter, 2021)[https://aclanthology.org/2021.inlg-1.23]).",
+      "original-evaluation": "BLEU is the only off-the-shelf metric commonly used.  Works have also used custom metrics like RG ([Wiseman et al, 2017](https://aclanthology.org/D17-1239)), and a recent shared task explored other metrics and their corrolation with human evaluation ([Thomson & Reiter, 2021](https://aclanthology.org/2021.inlg-1.23)).",
       "model-abilities": "Many, if not all aspects of data-to-text systems can be measured with this dataset.  It has complex data analytics, meaninful document planning (10-15 sentence documents with a narrative structure), as well as microplanning and realisation requirements.  Finding models to handle this volume of data, as well as methods for meaninfully evaluate generations is a very open question."
     }
   },
@@ -16,7 +16,7 @@
     "rationale": {
       "sole-task-dataset": "no",
       "distinction-description": "N/A",
-      "contribution": "This dataset contains a data analytics problem in the classic sense ((Reiter, 2007)[https://aclanthology.org/W07-2315]).  That is, there is a large amount of data from which insights need to be selected.  Further, the insights should be both from simple shallow queries (such as dirext transcriptions of the properties of a subject, i.e., a player and their statistics), as well as aggregated (how a player has done over time).  There is far more on the data side than is required to be realised, and indeed, could be practically realised.  This depth of data analytics problem does not exist in other datasets.",
+      "contribution": "This dataset contains a data analytics problem in the classic sense ([Reiter, 2007](https://aclanthology.org/W07-2315)).  That is, there is a large amount of data from which insights need to be selected.  Further, the insights should be both from simple shallow queries (such as dirext transcriptions of the properties of a subject, i.e., a player and their statistics), as well as aggregated (how a player has done over time).  There is far more on the data side than is required to be realised, and indeed, could be practically realised.  This depth of data analytics problem does not exist in other datasets.",
       "model-ability": "Many, if not all aspects of data-to-text systems can be measured with this dataset.  It has complex data analytics, meaninful document planning (10-15 sentence documents with a narrative structure), as well as microplanning and realisation requirements.  Finding models to handle this volume of data, as well as methods for meaninfully evaluate generations is a very open question."
     },
     "curation": {
@@ -28,7 +28,7 @@
       "additional-splits-capacicites": "N/A"
     },
     "starting": {
-      "research-pointers": "For dataset discussion see:\nThomson et al, 2020 https://aclanthology.org/2020.intellang-1.4/\n\nFor evaluation see:\nThomson & Reiter 2020\nThomson & Reiter (2021): https://aclanthology.org/2021.inlg-1.23\nKasner et al (2021): https://aclanthology.org/2021.inlg-1.25\n\nFor a system using the relational database form of SportSett, see:\nThomson et al (2020): https://aclanthology.org/2020.inlg-1.6/\n\nFor recent systems using the Rotowire dataset, see:\nPuduppully & Lapata (2021): https://github.com/ratishsp/data2text-macro-plan-py\nRebuffel et all (2020): https://github.com/KaijuML/data-to-text-hierarchical",
+      "research-pointers": "For dataset discussion see [Thomson et al, 2020](https://aclanthology.org/2020.intellang-1.4/)\n\nFor evaluation see:\n- [Thomson & Reiter 2020, Thomson & Reiter (2021)](https://aclanthology.org/2021.inlg-1.23)\n- [Kasner et al (2021)](https://aclanthology.org/2021.inlg-1.25)\n\nFor a system using the relational database form of SportSett, see:\n- [Thomson et al (2020)](https://aclanthology.org/2020.inlg-1.6/)\n\nFor recent systems using the Rotowire dataset, see:\n- [Puduppully & Lapata (2021)](https://github.com/ratishsp/data2text-macro-plan-py)\n- [Rebuffel et all (2020)](https://github.com/KaijuML/data-to-text-hierarchical)",
       "technical-terms": "N/A"
     }
   },
@@ -71,9 +71,9 @@
       "structure-example": "```\n{\n\t\"sportsett_id\": \"1\",\n\t\"gem_id\": \"GEM-sportsett_basketball-train-0\",\n\t\"game\": {\n\t\t\"day\": \"1\",\n\t\t\"month\": \"November\",\n\t\t\"year\": \"2014\",\n\t\t\"dayname\": \"Saturday\",\n\t\t\"season\": \"2014\",\n\t\t\"stadium\": \"Wells Fargo Center\",\n\t\t\"city\": \"Philadelphia\",\n\t\t\"state\": \"Pennsylvania\",\n\t\t\"attendance\": \"19753\",\n\t\t\"capacity\": \"20478\",\n\t\t\"game_id\": \"1\"\n\t},\n\t\"teams\": {\n\t\t\"home\": {\n\t\t\t\"name\": \"76ers\",\n\t\t\t\"place\": \"Philadelphia\",\n\t\t\t\"conference\": \"Eastern Conference\",\n\t\t\t\"division\": \"Atlantic\",\n\t\t\t\"wins\": \"0\",\n\t\t\t\"losses\": \"3\",\n\t\t\t\"conference_standing\": 15,\n\t\t\t\"game_number\": \"3\",\n\t\t\t\"previous_game_id\": \"42\",\n\t\t\t\"next_game_id\": \"2\",\n\t\t\t\"line_score\": {\n\t\t\t\t\"game\": {\n\t\t\t\t\t\"FG3A\": \"23\",\n\t\t\t\t\t\"FG3M\": \"7\",\n\t\t\t\t\t\"FG3_PCT\": \"30\",\n\t\t\t\t\t\"FGA\": \"67\",\n\t\t\t\t\t\"FGM\": \"35\",\n\t\t\t\t\t\"FG_PCT\": \"52\",\n\t\t\t\t\t\"FTA\": \"26\",\n\t\t\t\t\t\"FTM\": \"19\",\n\t\t\t\t\t\"FT_PCT\": \"73\",\n\t\t\t\t\t\"DREB\": \"33\",\n\t\t\t\t\t\"OREB\": \"4\",\n\t\t\t\t\t\"TREB\": \"37\",\n\t\t\t\t\t\"BLK\": \"10\",\n\t\t\t\t\t\"AST\": \"28\",\n\t\t\t\t\t\"STL\": \"9\",\n\t\t\t\t\t\"TOV\": \"24\",\n\t\t\t\t\t\"PF\": \"21\",\n\t\t\t\t\t\"PTS\": \"96\",\n\t\t\t\t\t\"MIN\": \"4\"\n\t\t\t\t},\n\t\t\t\t\"H1\": {\n\t\t\t\t\t\"FG3A\": \"82\",\n\t\t\t\t\t\"FG3M\": \"30\",\n\t\t\t\t\t\"FG3_PCT\": \"37\",\n\t\t\t\t\t\"FGA\": \"2115\",\n\t\t\t\t\t\"FGM\": \"138\",\n\t\t\t\t\t\"FG_PCT\": \"7\",\n\t\t\t\t\t\"FTA\": \"212\",\n\t\t\t\t\t\"FTM\": \"18\",\n\t\t\t\t\t\"FT_PCT\": \"8\",\n\t\t\t\t\t\"DREB\": \"810\",\n\t\t\t\t\t\"OREB\": \"21\",\n\t\t\t\t\t\"TREB\": \"831\",\n\t\t\t\t\t\"BLK\": \"51\",\n\t\t\t\t\t\"AST\": \"107\",\n\t\t\t\t\t\"STL\": \"21\",\n\t\t\t\t\t\"TOV\": \"64\",\n\t\t\t\t\t\"PTS\": \"3024\",\n\t\t\t\t\t\"MIN\": \"6060\"\n\t\t\t\t},\n\t\t\t\t\"H2\": {\n\t\t\t\t\t\"FG3A\": \"85\",\n\t\t\t\t\t\"FG3M\": \"40\",\n\t\t\t\t\t\"FG3_PCT\": \"47\",\n\t\t\t\t\t\"FGA\": \"1615\",\n\t\t\t\t\t\"FGM\": \"104\",\n\t\t\t\t\t\"FG_PCT\": \"6\",\n\t\t\t\t\t\"FTA\": \"66\",\n\t\t\t\t\t\"FTM\": \"55\",\n\t\t\t\t\t\"FT_PCT\": \"83\",\n\t\t\t\t\t\"DREB\": \"96\",\n\t\t\t\t\t\"OREB\": \"10\",\n\t\t\t\t\t\"TREB\": \"106\",\n\t\t\t\t\t\"BLK\": \"22\",\n\t\t\t\t\t\"AST\": \"92\",\n\t\t\t\t\t\"STL\": \"24\",\n\t\t\t\t\t\"TOV\": \"68\",\n\t\t\t\t\t\"PTS\": \"2913\",\n\t\t\t\t\t\"MIN\": \"6060\"\n\t\t\t\t},\n\t\t\t\t\"Q1\": {\n\t\t\t\t\t\"FG3A\": \"8\",\n\t\t\t\t\t\"FG3M\": \"3\",\n\t\t\t\t\t\"FG3_PCT\": \"38\",\n\t\t\t\t\t\"FGA\": \"21\",\n\t\t\t\t\t\"FGM\": \"13\",\n\t\t\t\t\t\"FG_PCT\": \"62\",\n\t\t\t\t\t\"FTA\": \"2\",\n\t\t\t\t\t\"FTM\": \"1\",\n\t\t\t\t\t\"FT_PCT\": \"50\",\n\t\t\t\t\t\"DREB\": \"8\",\n\t\t\t\t\t\"OREB\": \"2\",\n\t\t\t\t\t\"TREB\": \"10\",\n\t\t\t\t\t\"BLK\": \"5\",\n\t\t\t\t\t\"AST\": \"10\",\n\t\t\t\t\t\"STL\": \"2\",\n\t\t\t\t\t\"TOV\": \"6\",\n\t\t\t\t\t\"PTS\": \"30\",\n\t\t\t\t\t\"MIN\": \"60\"\n\t\t\t\t},\n\t\t\t\t\"Q2\": {\n\t\t\t\t\t\"FG3A\": \"2\",\n\t\t\t\t\t\"FG3M\": \"0\",\n\t\t\t\t\t\"FG3_PCT\": \"0\",\n\t\t\t\t\t\"FGA\": \"15\",\n\t\t\t\t\t\"FGM\": \"8\",\n\t\t\t\t\t\"FG_PCT\": \"53\",\n\t\t\t\t\t\"FTA\": \"12\",\n\t\t\t\t\t\"FTM\": \"8\",\n\t\t\t\t\t\"FT_PCT\": \"67\",\n\t\t\t\t\t\"DREB\": \"10\",\n\t\t\t\t\t\"OREB\": \"1\",\n\t\t\t\t\t\"TREB\": \"11\",\n\t\t\t\t\t\"BLK\": \"1\",\n\t\t\t\t\t\"AST\": \"7\",\n\t\t\t\t\t\"STL\": \"1\",\n\t\t\t\t\t\"TOV\": \"4\",\n\t\t\t\t\t\"PTS\": \"24\",\n\t\t\t\t\t\"MIN\": \"60\"\n\t\t\t\t},\n\t\t\t\t\"Q3\": {\n\t\t\t\t\t\"FG3A\": \"8\",\n\t\t\t\t\t\"FG3M\": \"4\",\n\t\t\t\t\t\"FG3_PCT\": \"50\",\n\t\t\t\t\t\"FGA\": \"16\",\n\t\t\t\t\t\"FGM\": \"10\",\n\t\t\t\t\t\"FG_PCT\": \"62\",\n\t\t\t\t\t\"FTA\": \"6\",\n\t\t\t\t\t\"FTM\": \"5\",\n\t\t\t\t\t\"FT_PCT\": \"83\",\n\t\t\t\t\t\"DREB\": \"9\",\n\t\t\t\t\t\"OREB\": \"1\",\n\t\t\t\t\t\"TREB\": \"10\",\n\t\t\t\t\t\"BLK\": \"2\",\n\t\t\t\t\t\"AST\": \"9\",\n\t\t\t\t\t\"STL\": \"2\",\n\t\t\t\t\t\"TOV\": \"6\",\n\t\t\t\t\t\"PTS\": \"29\",\n\t\t\t\t\t\"MIN\": \"60\"\n\t\t\t\t},\n\t\t\t\t\"Q4\": {\n\t\t\t\t\t\"FG3A\": \"5\",\n\t\t\t\t\t\"FG3M\": \"0\",\n\t\t\t\t\t\"FG3_PCT\": \"0\",\n\t\t\t\t\t\"FGA\": \"15\",\n\t\t\t\t\t\"FGM\": \"4\",\n\t\t\t\t\t\"FG_PCT\": \"27\",\n\t\t\t\t\t\"FTA\": \"6\",\n\t\t\t\t\t\"FTM\": \"5\",\n\t\t\t\t\t\"FT_PCT\": \"83\",\n\t\t\t\t\t\"DREB\": \"6\",\n\t\t\t\t\t\"OREB\": \"0\",\n\t\t\t\t\t\"TREB\": \"6\",\n\t\t\t\t\t\"BLK\": \"2\",\n\t\t\t\t\t\"AST\": \"2\",\n\t\t\t\t\t\"STL\": \"4\",\n\t\t\t\t\t\"TOV\": \"8\",\n\t\t\t\t\t\"PTS\": \"13\",\n\t\t\t\t\t\"MIN\": \"60\"\n\t\t\t\t},\n\t\t\t\t\"OT\": {\n\t\t\t\t\t\"FG3A\": \"0\",\n\t\t\t\t\t\"FG3M\": \"0\",\n\t\t\t\t\t\"FG3_PCT\": \"0\",\n\t\t\t\t\t\"FGA\": \"0\",\n\t\t\t\t\t\"FGM\": \"0\",\n\t\t\t\t\t\"FG_PCT\": \"0\",\n\t\t\t\t\t\"FTA\": \"0\",\n\t\t\t\t\t\"FTM\": \"0\",\n\t\t\t\t\t\"FT_PCT\": \"0\",\n\t\t\t\t\t\"DREB\": \"0\",\n\t\t\t\t\t\"OREB\": \"0\",\n\t\t\t\t\t\"TREB\": \"0\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"AST\": \"0\",\n\t\t\t\t\t\"STL\": \"0\",\n\t\t\t\t\t\"TOV\": \"0\",\n\t\t\t\t\t\"PTS\": \"0\",\n\t\t\t\t\t\"MIN\": \"0\"\n\t\t\t\t}\n\t\t\t},\n\t\t\t\"box_score\": [\n\t\t\t\t{\n\t\t\t\t\t\"first_name\": \"Tony\",\n\t\t\t\t\t\"last_name\": \"Wroten\",\n\t\t\t\t\t\"name\": \"Tony Wroten\",\n\t\t\t\t\t\"starter\": \"True\",\n\t\t\t\t\t\"MIN\": \"33\",\n\t\t\t\t\t\"FGM\": \"6\",\n\t\t\t\t\t\"FGA\": \"11\",\n\t\t\t\t\t\"FG_PCT\": \"55\",\n\t\t\t\t\t\"FG3M\": \"1\",\n\t\t\t\t\t\"FG3A\": \"4\",\n\t\t\t\t\t\"FG3_PCT\": \"25\",\n\t\t\t\t\t\"FTM\": \"8\",\n\t\t\t\t\t\"FTA\": \"11\",\n\t\t\t\t\t\"FT_PCT\": \"73\",\n\t\t\t\t\t\"OREB\": \"0\",\n\t\t\t\t\t\"DREB\": \"3\",\n\t\t\t\t\t\"TREB\": \"3\",\n\t\t\t\t\t\"AST\": \"10\",\n\t\t\t\t\t\"STL\": \"1\",\n\t\t\t\t\t\"BLK\": \"1\",\n\t\t\t\t\t\"TOV\": \"4\",\n\t\t\t\t\t\"PF\": 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\"Chalmers\",\n\t\t\t\t\t\"name\": \"Mario Chalmers\",\n\t\t\t\t\t\"starter\": \"False\",\n\t\t\t\t\t\"MIN\": \"25\",\n\t\t\t\t\t\"FGM\": \"6\",\n\t\t\t\t\t\"FGA\": \"9\",\n\t\t\t\t\t\"FG_PCT\": \"67\",\n\t\t\t\t\t\"FG3M\": \"2\",\n\t\t\t\t\t\"FG3A\": \"2\",\n\t\t\t\t\t\"FG3_PCT\": \"100\",\n\t\t\t\t\t\"FTM\": \"6\",\n\t\t\t\t\t\"FTA\": \"10\",\n\t\t\t\t\t\"FT_PCT\": \"60\",\n\t\t\t\t\t\"OREB\": \"0\",\n\t\t\t\t\t\"DREB\": \"2\",\n\t\t\t\t\t\"TREB\": \"2\",\n\t\t\t\t\t\"AST\": \"4\",\n\t\t\t\t\t\"STL\": \"4\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"TOV\": \"0\",\n\t\t\t\t\t\"PF\": \"1\",\n\t\t\t\t\t\"PTS\": \"20\",\n\t\t\t\t\t\"+/-\": \"18\",\n\t\t\t\t\t\"DOUBLE\": \"none\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"first_name\": \"Shabazz\",\n\t\t\t\t\t\"last_name\": \"Napier\",\n\t\t\t\t\t\"name\": \"Shabazz Napier\",\n\t\t\t\t\t\"starter\": \"False\",\n\t\t\t\t\t\"MIN\": \"20\",\n\t\t\t\t\t\"FGM\": \"2\",\n\t\t\t\t\t\"FGA\": \"3\",\n\t\t\t\t\t\"FG_PCT\": \"67\",\n\t\t\t\t\t\"FG3M\": \"1\",\n\t\t\t\t\t\"FG3A\": \"2\",\n\t\t\t\t\t\"FG3_PCT\": \"50\",\n\t\t\t\t\t\"FTM\": \"0\",\n\t\t\t\t\t\"FTA\": \"0\",\n\t\t\t\t\t\"FT_PCT\": \"0\",\n\t\t\t\t\t\"OREB\": \"0\",\n\t\t\t\t\t\"DREB\": \"3\",\n\t\t\t\t\t\"TREB\": \"3\",\n\t\t\t\t\t\"AST\": \"4\",\n\t\t\t\t\t\"STL\": \"2\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"TOV\": \"1\",\n\t\t\t\t\t\"PF\": \"4\",\n\t\t\t\t\t\"PTS\": \"5\",\n\t\t\t\t\t\"+/-\": \"11\",\n\t\t\t\t\t\"DOUBLE\": \"none\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"first_name\": \"Chris\",\n\t\t\t\t\t\"last_name\": \"Andersen\",\n\t\t\t\t\t\"name\": \"Chris Andersen\",\n\t\t\t\t\t\"starter\": \"False\",\n\t\t\t\t\t\"MIN\": \"17\",\n\t\t\t\t\t\"FGM\": \"0\",\n\t\t\t\t\t\"FGA\": \"2\",\n\t\t\t\t\t\"FG_PCT\": \"0\",\n\t\t\t\t\t\"FG3M\": \"0\",\n\t\t\t\t\t\"FG3A\": \"0\",\n\t\t\t\t\t\"FG3_PCT\": \"0\",\n\t\t\t\t\t\"FTM\": \"0\",\n\t\t\t\t\t\"FTA\": \"0\",\n\t\t\t\t\t\"FT_PCT\": \"0\",\n\t\t\t\t\t\"OREB\": \"1\",\n\t\t\t\t\t\"DREB\": \"2\",\n\t\t\t\t\t\"TREB\": \"3\",\n\t\t\t\t\t\"AST\": \"0\",\n\t\t\t\t\t\"STL\": \"0\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"TOV\": \"0\",\n\t\t\t\t\t\"PF\": \"2\",\n\t\t\t\t\t\"PTS\": \"0\",\n\t\t\t\t\t\"+/-\": \"6\",\n\t\t\t\t\t\"DOUBLE\": \"none\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"first_name\": \"Josh\",\n\t\t\t\t\t\"last_name\": \"McRoberts\",\n\t\t\t\t\t\"name\": \"Josh McRoberts\",\n\t\t\t\t\t\"starter\": \"False\",\n\t\t\t\t\t\"MIN\": \"11\",\n\t\t\t\t\t\"FGM\": \"1\",\n\t\t\t\t\t\"FGA\": \"3\",\n\t\t\t\t\t\"FG_PCT\": \"33\",\n\t\t\t\t\t\"FG3M\": \"0\",\n\t\t\t\t\t\"FG3A\": \"1\",\n\t\t\t\t\t\"FG3_PCT\": \"0\",\n\t\t\t\t\t\"FTM\": \"1\",\n\t\t\t\t\t\"FTA\": \"2\",\n\t\t\t\t\t\"FT_PCT\": \"50\",\n\t\t\t\t\t\"OREB\": \"0\",\n\t\t\t\t\t\"DREB\": \"3\",\n\t\t\t\t\t\"TREB\": \"3\",\n\t\t\t\t\t\"AST\": \"0\",\n\t\t\t\t\t\"STL\": \"0\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"TOV\": \"2\",\n\t\t\t\t\t\"PF\": \"3\",\n\t\t\t\t\t\"PTS\": \"3\",\n\t\t\t\t\t\"+/-\": \"1\",\n\t\t\t\t\t\"DOUBLE\": \"none\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"first_name\": \"James\",\n\t\t\t\t\t\"last_name\": \"Ennis\",\n\t\t\t\t\t\"name\": \"James Ennis\",\n\t\t\t\t\t\"starter\": \"False\",\n\t\t\t\t\t\"MIN\": \"7\",\n\t\t\t\t\t\"FGM\": \"2\",\n\t\t\t\t\t\"FGA\": \"3\",\n\t\t\t\t\t\"FG_PCT\": \"67\",\n\t\t\t\t\t\"FG3M\": \"1\",\n\t\t\t\t\t\"FG3A\": \"1\",\n\t\t\t\t\t\"FG3_PCT\": \"100\",\n\t\t\t\t\t\"FTM\": \"0\",\n\t\t\t\t\t\"FTA\": \"0\",\n\t\t\t\t\t\"FT_PCT\": \"0\",\n\t\t\t\t\t\"OREB\": \"1\",\n\t\t\t\t\t\"DREB\": \"1\",\n\t\t\t\t\t\"TREB\": \"2\",\n\t\t\t\t\t\"AST\": \"1\",\n\t\t\t\t\t\"STL\": \"0\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"TOV\": \"0\",\n\t\t\t\t\t\"PF\": \"1\",\n\t\t\t\t\t\"PTS\": \"5\",\n\t\t\t\t\t\"+/-\": \"2\",\n\t\t\t\t\t\"DOUBLE\": \"none\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"first_name\": \"Justin\",\n\t\t\t\t\t\"last_name\": \"Hamilton\",\n\t\t\t\t\t\"name\": \"Justin Hamilton\",\n\t\t\t\t\t\"starter\": \"False\",\n\t\t\t\t\t\"MIN\": \"5\",\n\t\t\t\t\t\"FGM\": \"1\",\n\t\t\t\t\t\"FGA\": \"1\",\n\t\t\t\t\t\"FG_PCT\": \"100\",\n\t\t\t\t\t\"FG3M\": \"0\",\n\t\t\t\t\t\"FG3A\": \"0\",\n\t\t\t\t\t\"FG3_PCT\": \"0\",\n\t\t\t\t\t\"FTM\": \"0\",\n\t\t\t\t\t\"FTA\": \"0\",\n\t\t\t\t\t\"FT_PCT\": \"0\",\n\t\t\t\t\t\"OREB\": \"1\",\n\t\t\t\t\t\"DREB\": \"1\",\n\t\t\t\t\t\"TREB\": \"2\",\n\t\t\t\t\t\"AST\": \"0\",\n\t\t\t\t\t\"STL\": \"0\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"TOV\": \"1\",\n\t\t\t\t\t\"PF\": \"0\",\n\t\t\t\t\t\"PTS\": \"2\",\n\t\t\t\t\t\"+/-\": \"3\",\n\t\t\t\t\t\"DOUBLE\": \"none\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"first_name\": \"Andre\",\n\t\t\t\t\t\"last_name\": \"Dawkins\",\n\t\t\t\t\t\"name\": \"Andre Dawkins\",\n\t\t\t\t\t\"starter\": \"False\",\n\t\t\t\t\t\"MIN\": \"1\",\n\t\t\t\t\t\"FGM\": \"0\",\n\t\t\t\t\t\"FGA\": \"0\",\n\t\t\t\t\t\"FG_PCT\": \"0\",\n\t\t\t\t\t\"FG3M\": \"0\",\n\t\t\t\t\t\"FG3A\": \"0\",\n\t\t\t\t\t\"FG3_PCT\": \"0\",\n\t\t\t\t\t\"FTM\": \"0\",\n\t\t\t\t\t\"FTA\": \"0\",\n\t\t\t\t\t\"FT_PCT\": \"0\",\n\t\t\t\t\t\"OREB\": \"0\",\n\t\t\t\t\t\"DREB\": \"0\",\n\t\t\t\t\t\"TREB\": \"0\",\n\t\t\t\t\t\"AST\": \"0\",\n\t\t\t\t\t\"STL\": \"0\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"TOV\": \"1\",\n\t\t\t\t\t\"PF\": \"1\",\n\t\t\t\t\t\"PTS\": \"0\",\n\t\t\t\t\t\"+/-\": \"0\",\n\t\t\t\t\t\"DOUBLE\": \"none\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"first_name\": \"Shannon\",\n\t\t\t\t\t\"last_name\": \"Brown\",\n\t\t\t\t\t\"name\": \"Shannon Brown\",\n\t\t\t\t\t\"starter\": \"False\",\n\t\t\t\t\t\"MIN\": \"0\",\n\t\t\t\t\t\"FGM\": \"0\",\n\t\t\t\t\t\"FGA\": \"0\",\n\t\t\t\t\t\"FG_PCT\": \"0\",\n\t\t\t\t\t\"FG3M\": \"0\",\n\t\t\t\t\t\"FG3A\": \"0\",\n\t\t\t\t\t\"FG3_PCT\": \"0\",\n\t\t\t\t\t\"FTM\": \"0\",\n\t\t\t\t\t\"FTA\": \"0\",\n\t\t\t\t\t\"FT_PCT\": \"0\",\n\t\t\t\t\t\"OREB\": \"0\",\n\t\t\t\t\t\"DREB\": \"0\",\n\t\t\t\t\t\"TREB\": \"0\",\n\t\t\t\t\t\"AST\": \"0\",\n\t\t\t\t\t\"STL\": \"0\",\n\t\t\t\t\t\"BLK\": \"0\",\n\t\t\t\t\t\"TOV\": \"0\",\n\t\t\t\t\t\"PF\": \"0\",\n\t\t\t\t\t\"PTS\": \"0\",\n\t\t\t\t\t\"+/-\": \"0\",\n\t\t\t\t\t\"DOUBLE\": \"none\"\n\t\t\t\t}\n\t\t\t],\n\t\t\t\"next_game\": {\n\t\t\t\t\"day\": \"2\",\n\t\t\t\t\"month\": \"November\",\n\t\t\t\t\"year\": \"2014\",\n\t\t\t\t\"dayname\": \"Sunday\",\n\t\t\t\t\"stadium\": \"American Airlines Arena\",\n\t\t\t\t\"city\": \"Miami\",\n\t\t\t\t\"opponent_name\": \"Raptors\",\n\t\t\t\t\"opponent_place\": \"Toronto\",\n\t\t\t\t\"is_home\": \"True\"\n\t\t\t}\n\t\t}\n\t},\n\t\"summaries\": [\n\t\t\"The Miami Heat ( 20 ) defeated the Philadelphia 76ers ( 0 - 3 ) 114 - 96 on Saturday . Chris Bosh scored a game - high 30 points to go with eight rebounds in 33 minutes . Josh McRoberts made his Heat debut after missing the entire preseason recovering from toe surgery . McRoberts came off the bench and played 11 minutes . Shawne Williams was once again the starter at power forward in McRoberts ' stead . Williams finished with 15 points and three three - pointers in 29 minutes . Mario Chalmers scored 18 points in 25 minutes off the bench . Luc Richard Mbah a Moute replaced Chris Johnson in the starting lineup for the Sixers on Saturday . Hollis Thompson shifted down to the starting shooting guard job to make room for Mbah a Moute . Mbah a Moute finished with nine points and seven rebounds in 19 minutes . K.J . McDaniels , who suffered a minor hip flexor injury in Friday 's game , was available and played 21 minutes off the bench , finishing with eight points and three blocks . Michael Carter-Williams is expected to be out until Nov. 13 , but Tony Wroten continues to put up impressive numbers in Carter-Williams ' absence . Wroten finished with a double - double of 21 points and 10 assists in 33 minutes . The Heat will complete a back - to - back set at home Sunday against the Tornoto Raptors . The Sixers ' next game is at home Monday against the Houston Rockets .\"\n\t]\n}\n```",
       "structure-splits": "- Train: NBA seasons - 2014, 2015, & 2016; total instances - 3690\n- Validation: NBA seasons - 2017; total instances - 1230\n- Test: NBA seasons - 2018; total instances - 1230",
       "structure-splits-criteria": "The splits were created as per different NBA seasons. All the games in regular season (no play-offs) are added in the dataset",
-      "data-fields": "Each instance in the dataset has five fields. \n\n1. \"sportsett_id\": This is a unique id as used in the original SportSett database. It starts with '1' with the first instance in the train-set and ends with '6150' with the last instance in test-set.\n\n2. \"gem_id\": This is a unique id created as per GEM's requirement which follows the `GEM-${DATASET_NAME}-${SPLIT-NAME}-${id}` pattern.\n\n3. \"game\": This field contains a dictionary with information about current game. It has information such as date on which the game was played alongwith the stadium, city, state  where it was played.\n\n4. \"teams\": This filed is a dictionary of multiple nested dictionaries. On the highest level, it has two keys: 'home' and 'vis', which provide the stats for home team and visiting team of the game. Both are dictionaries with same structure. Each dictionary will contain team's information such as name of the team, their total wins/losses in current season, their conference standing, the SportSett ids for their current and previous games. Apart from these general information, they also have the box- and line- scores for the team in the game. Box score is the stats of players from the team at the end of the game, while line score along with the whole game stats is divided into quarters and halves as well as the extra-time (if happened in the game). After these scores, there is another field of next-game, which gives general information about team's next game such as the place and opponent's name of the next game.\n\n5. \"summaries\": This is a list of summaries for each game. Some games will have more than one summary, in that case, the list will have more than one entries. Each summary in the list is a string which can be tokenised by a space, following the practices in RotoWire-FG dataset ((Wang, 2019)[https://www.aclweb.org/anthology/W19-8639]).",
-      "structure-description": "The structure mostly follows the original structure defined in RotoWire dataset ((Wiseman et. al. 2017)[https://aclanthology.org/D17-1239/]) with some modifications (such as game and next-game keys) address the problem of information gap between input and output data ((Thomson et. al. 2020)[https://aclanthology.org/2020.inlg-1.6/]). ",
-      "structure-labels": "Similar to RotoWire dataset ((Wiseman et. al. 2017)[https://aclanthology.org/D17-1239/]) ",
+      "data-fields": "Each instance in the dataset has five fields. \n\n1. \"sportsett_id\": This is a unique id as used in the original SportSett database. It starts with '1' with the first instance in the train-set and ends with '6150' with the last instance in test-set.\n\n2. \"gem_id\": This is a unique id created as per GEM's requirement which follows the `GEM-${DATASET_NAME}-${SPLIT-NAME}-${id}` pattern.\n\n3. \"game\": This field contains a dictionary with information about current game. It has information such as date on which the game was played alongwith the stadium, city, state  where it was played.\n\n4. \"teams\": This filed is a dictionary of multiple nested dictionaries. On the highest level, it has two keys: 'home' and 'vis', which provide the stats for home team and visiting team of the game. Both are dictionaries with same structure. Each dictionary will contain team's information such as name of the team, their total wins/losses in current season, their conference standing, the SportSett ids for their current and previous games. Apart from these general information, they also have the box- and line- scores for the team in the game. Box score is the stats of players from the team at the end of the game, while line score along with the whole game stats is divided into quarters and halves as well as the extra-time (if happened in the game). After these scores, there is another field of next-game, which gives general information about team's next game such as the place and opponent's name of the next game.\n\n5. \"summaries\": This is a list of summaries for each game. Some games will have more than one summary, in that case, the list will have more than one entries. Each summary in the list is a string which can be tokenised by a space, following the practices in RotoWire-FG dataset ([Wang, 2019](https://www.aclweb.org/anthology/W19-8639)).",
+      "structure-description": "The structure mostly follows the original structure defined in RotoWire dataset ([Wiseman et. al. 2017](https://aclanthology.org/D17-1239/)) with some modifications (such as game and next-game keys) address the problem of information gap between input and output data ([Thomson et. al. 2020](https://aclanthology.org/2020.inlg-1.6/)).",
+      "structure-labels": "Similar to RotoWire dataset ([Wiseman et. al. 2017](https://aclanthology.org/D17-1239/))",
       "structure-outlier": "N/A"
     },
     "what": {
@@ -84,8 +84,8 @@
     "original": {
       "is-aggregated": "yes",
       "aggregated-sources": "RotoWire-FG (https://www.rotowire.com).\nWikipedia (https://en.wikipedia.org/wiki/Main_Page)\nBasketball Reference (https://www.basketball-reference.com)\n",
-      "rationale": "The references texts were taken from the existing dataset RotoWire-FG (Wang, 2019: https://www.aclweb.org/anthology/W19-8639), which is in turn based on Rotowire (Wiseman et al, 2017: https://aclanthology.org/D17-1239).  The rationale behind this dataset was to re-structure the data such that aggregate statistics over multiple games, as well as upcoming game schedules could be included, moving the dataset from snapshots of single games, to a format where almost everything that could be present in the reference texts could be found in the data.",
-      "communicative": "Create a summary of a basketball, with insightful facts about the game, teams, and players, both within the game, withing periods during the game, and over the course of seasons/careers where appropriate.  This is a data-to-text problem in the classic sense (Reiter, 2007: https://aclanthology.org/W07-2315) in that it has a difficult data analystics state, in addition to ordering and transcription of selected facts."
+      "rationale": "The references texts were taken from the existing dataset RotoWire-FG ([Wang, 2019](https://www.aclweb.org/anthology/W19-8639)), which is in turn based on Rotowire ([Wiseman et al, 2017](https://aclanthology.org/D17-1239)).  The rationale behind this dataset was to re-structure the data such that aggregate statistics over multiple games, as well as upcoming game schedules could be included, moving the dataset from snapshots of single games, to a format where almost everything that could be present in the reference texts could be found in the data.",
+      "communicative": "Create a summary of a basketball, with insightful facts about the game, teams, and players, both within the game, withing periods during the game, and over the course of seasons/careers where appropriate.  This is a data-to-text problem in the classic sense ([Reiter, 2007](https://aclanthology.org/W07-2315)) in that it has a difficult data analystics state, in addition to ordering and transcription of selected facts."
     },
     "language": {
       "found": [