Datasets:

Modalities:
Text
Formats:
parquet
Libraries:
Datasets
pandas
License:
Guilleber commited on
Commit
0a3e4f5
1 Parent(s): ae60ae3

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +69 -0
README.md CHANGED
@@ -129,4 +129,73 @@ configs:
129
  path: papyrus-m/test-*
130
  - split: validation
131
  path: papyrus-m/validation-*
 
 
 
 
 
 
 
 
 
 
132
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
129
  path: papyrus-m/test-*
130
  - split: validation
131
  path: papyrus-m/validation-*
132
+ license: apache-2.0
133
+ language:
134
+ - en
135
+ - fr
136
+ tags:
137
+ - text-to-text
138
+ - keyphrase-generation
139
+ pretty_name: Papyrus
140
+ size_categories:
141
+ - 10K<n<100K
142
  ---
143
+
144
+
145
+ # Dataset Card for Papyrus
146
+
147
+ - **Paper:** [A new dataset for multilingual keyphrase generation](https://proceedings.neurips.cc/paper_files/paper/2022/hash/f88709551258331f9ab31b33c71021a4-Abstract-Datasets_and_Benchmarks.html)
148
+ - **Github:** <https://github.com/smolPixel/French-keyphrase-generation>
149
+
150
+
151
+ ## Dataset Description
152
+
153
+ ### Dataset Summary
154
+
155
+ The datasets are derived from Papyrus, a repository at Université de Montréal containing various types of documents, mainly theses with abstracts in multiple languages, primarily French and English. The entries are provided in four different configurations based on the languages of abstracts, allowing for generating keyphrases in French, English, or multiple languages.
156
+ - **Papyrus-f:** From the French abstracts, generate French keyphrases.
157
+ - **Papyrus-e:** From the English abstracts, generate English keyphrases.
158
+ - **Papyrus-m:** From one abstract in any language, generate keyphrases in that same
159
+ language (one language to one language).
160
+ - **Papyrus-a:** From the multiple abstracts of a document, generate keyphrases in the
161
+ same languages as the abstracts (many to many languages).
162
+
163
+ ### Languages
164
+
165
+ - **Main languages:** English, French
166
+ - **Others:** Spanish, German, Italian, Portuguese, Arabic, Tagalog, Catalan, Greek, Turkish, Russian, Polish, Farsi, Indonesian, Lingala, Swedish, Finnish, Romanian, Korean
167
+
168
+
169
+ ## Dataset Structure
170
+
171
+ ### Dataset content
172
+
173
+ | Config | Train set size | Valid. set size | Test set size |
174
+ | --------- | -------------- | --------------- | ------------- |
175
+ | papyrus-m | 20963 | 3040 | 6061 |
176
+ | papyrus-e | 10508 | 1539 | 3046 |
177
+ | papyrus-f | 10299 | 1488 | 2981 |
178
+ | papyrus-a | 11290 | 1638 | 3261 |
179
+
180
+ ### Data fields
181
+
182
+ - **doc_id:** a unique id for the original document.
183
+ - **title:** title of the thesis or article (the language of the title does not always match the language of the abstract/keyphrases).
184
+ - **input_text:** abstract of the document.
185
+ - **keyphrases:** associated keyphrases.
186
+ - **lang:** language of the abstract/keyphrases.
187
+
188
+
189
+ ## Citation
190
+
191
+ @inproceedings{NEURIPS2022_f8870955,
192
+ author = {Piedboeuf, Fr\'{e}d\'{e}ric and Langlais, Philippe},
193
+ booktitle = {Advances in Neural Information Processing Systems},
194
+ editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
195
+ pages = {38046--38059},
196
+ publisher = {Curran Associates, Inc.},
197
+ title = {A new dataset for multilingual keyphrase generation},
198
+ url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/f88709551258331f9ab31b33c71021a4-Paper-Datasets_and_Benchmarks.pdf},
199
+ volume = {35},
200
+ year = {2022}
201
+ }