Datasets:
GEM
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README.md CHANGED
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1
+ ---
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+ annotations_creators:
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+ - found
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+ language_creators:
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+ - unknown
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+ language:
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+ - de
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+ - en
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+ - fr
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+ - cs
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+ license:
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+ - cc-by-sa-4.0
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+ multilinguality:
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+ - unknown
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+ size_categories:
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+ - unknown
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - summarization
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+ task_ids: []
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+ pretty_name: xwikis
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+ ---
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+
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+ # Dataset Card for GEM/xwikis
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** https://github.com/lauhaide/clads
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+ - **Repository:** [Needs More Information]
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+ - **Paper:** https://arxiv.org/abs/2202.09583
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+ - **Leaderboard:** N/A
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+ - **Point of Contact:** Laura Perez-Beltrachini
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+
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+ ### Link to Main Data Card
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+
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+ You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xwikis).
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+
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+ ### Dataset Summary
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+
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+ The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation.
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+
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+ You can load the dataset via:
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+ ```
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+ import datasets
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+ data = datasets.load_dataset('GEM/xwikis')
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+ ```
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+ The data loader can be found [here](https://huggingface.co/datasets/GEM/xwikis).
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+
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+ #### website
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+ [Github](https://github.com/lauhaide/clads)
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+
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+ #### paper
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+ https://arxiv.org/abs/2202.09583
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+
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+ #### authors
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+ Laura Perez-Beltrachini (University of Edinburgh)
58
+
59
+ ## Dataset Overview
60
+
61
+ ### Where to find the Data and its Documentation
62
+
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+ #### Webpage
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+
65
+ <!-- info: What is the webpage for the dataset (if it exists)? -->
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+ <!-- scope: telescope -->
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+ [Github](https://github.com/lauhaide/clads)
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+
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+ #### Paper
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+
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+ <!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
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+ <!-- scope: telescope -->
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+ https://arxiv.org/abs/2202.09583
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+
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+ #### BibTex
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+
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+ <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
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+ <!-- scope: microscope -->
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+ ```
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+ @InProceedings{clads-emnlp,
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+ author = "Laura Perez-Beltrachini and Mirella Lapata",
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+ title = "Models and Datasets for Cross-Lingual Summarisation",
83
+ booktitle = "Proceedings of The 2021 Conference on Empirical Methods in Natural Language Processing ",
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+ year = "2021",
85
+ address = "Punta Cana, Dominican Republic",
86
+ }
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+ ```
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+
89
+ #### Contact Name
90
+
91
+ <!-- quick -->
92
+ <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
93
+ <!-- scope: periscope -->
94
+ Laura Perez-Beltrachini
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+
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+ #### Contact Email
97
+
98
+ <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
99
+ <!-- scope: periscope -->
100
+ lperez@ed.ac.uk
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+
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+ #### Has a Leaderboard?
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+
104
+ <!-- info: Does the dataset have an active leaderboard? -->
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+ <!-- scope: telescope -->
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+ no
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+
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+
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+ ### Languages and Intended Use
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+
111
+ #### Multilingual?
112
+
113
+ <!-- quick -->
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+ <!-- info: Is the dataset multilingual? -->
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+ <!-- scope: telescope -->
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+ yes
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+
118
+ #### Covered Languages
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+
120
+ <!-- quick -->
121
+ <!-- info: What languages/dialects are covered in the dataset? -->
122
+ <!-- scope: telescope -->
123
+ `German`, `English`, `French`, `Czech`
124
+
125
+ #### License
126
+
127
+ <!-- quick -->
128
+ <!-- info: What is the license of the dataset? -->
129
+ <!-- scope: telescope -->
130
+ cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
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+
132
+ #### Intended Use
133
+
134
+ <!-- info: What is the intended use of the dataset? -->
135
+ <!-- scope: microscope -->
136
+ Cross-lingual and Multi-lingual single long input document abstractive summarisation.
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+
138
+ #### Primary Task
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+
140
+ <!-- info: What primary task does the dataset support? -->
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+ <!-- scope: telescope -->
142
+ Summarization
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+
144
+ #### Communicative Goal
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+
146
+ <!-- quick -->
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+ <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
148
+ <!-- scope: periscope -->
149
+ Entity descriptive summarisation, that is, generate a summary that conveys the most salient facts of a document related to a given entity.
150
+
151
+
152
+ ### Credit
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+
154
+ #### Curation Organization Type(s)
155
+
156
+ <!-- info: In what kind of organization did the dataset curation happen? -->
157
+ <!-- scope: telescope -->
158
+ `academic`
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+
160
+ #### Dataset Creators
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+
162
+ <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
163
+ <!-- scope: microscope -->
164
+ Laura Perez-Beltrachini (University of Edinburgh)
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+
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+ #### Who added the Dataset to GEM?
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+
168
+ <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
169
+ <!-- scope: microscope -->
170
+ Laura Perez-Beltrachini (University of Edinburgh) and Ronald Cardenas (University of Edinburgh)
171
+
172
+
173
+ ### Dataset Structure
174
+
175
+ #### Data Splits
176
+
177
+ <!-- info: Describe and name the splits in the dataset if there are more than one. -->
178
+ <!-- scope: periscope -->
179
+ For each language pair and direction there exists a train/valid/test split.
180
+ The test split is a sample of size 7k from the intersection of titles existing in the four languages (cs,fr,en,de).
181
+ Train/valid are randomly split.
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+
183
+
184
+
185
+ ## Dataset in GEM
186
+
187
+ ### Rationale for Inclusion in GEM
188
+
189
+ #### Similar Datasets
190
+
191
+ <!-- info: Do other datasets for the high level task exist? -->
192
+ <!-- scope: telescope -->
193
+ no
194
+
195
+
196
+ ### GEM-Specific Curation
197
+
198
+ #### Modificatied for GEM?
199
+
200
+ <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
201
+ <!-- scope: telescope -->
202
+ no
203
+
204
+ #### Additional Splits?
205
+
206
+ <!-- info: Does GEM provide additional splits to the dataset? -->
207
+ <!-- scope: telescope -->
208
+ no
209
+
210
+
211
+ ### Getting Started with the Task
212
+
213
+
214
+
215
+
216
+ ## Previous Results
217
+
218
+ ### Previous Results
219
+
220
+ #### Measured Model Abilities
221
+
222
+ <!-- info: What aspect of model ability can be measured with this dataset? -->
223
+ <!-- scope: telescope -->
224
+ - identification of entity salient information
225
+ - translation
226
+ - multi-linguality
227
+ - cross-lingual transfer, zero-shot, few-shot
228
+
229
+ #### Metrics
230
+
231
+ <!-- info: What metrics are typically used for this task? -->
232
+ <!-- scope: periscope -->
233
+ `ROUGE`
234
+
235
+ #### Previous results available?
236
+
237
+ <!-- info: Are previous results available? -->
238
+ <!-- scope: telescope -->
239
+ yes
240
+
241
+ #### Other Evaluation Approaches
242
+
243
+ <!-- info: What evaluation approaches have others used? -->
244
+ <!-- scope: periscope -->
245
+ ROUGE-1/2/L
246
+
247
+
248
+
249
+ ## Dataset Curation
250
+
251
+ ### Original Curation
252
+
253
+ #### Sourced from Different Sources
254
+
255
+ <!-- info: Is the dataset aggregated from different data sources? -->
256
+ <!-- scope: telescope -->
257
+ no
258
+
259
+
260
+ ### Language Data
261
+
262
+ #### How was Language Data Obtained?
263
+
264
+ <!-- info: How was the language data obtained? -->
265
+ <!-- scope: telescope -->
266
+ `Found`
267
+
268
+ #### Where was it found?
269
+
270
+ <!-- info: If found, where from? -->
271
+ <!-- scope: telescope -->
272
+ `Single website`
273
+
274
+ #### Data Validation
275
+
276
+ <!-- info: Was the text validated by a different worker or a data curator? -->
277
+ <!-- scope: telescope -->
278
+ other
279
+
280
+ #### Was Data Filtered?
281
+
282
+ <!-- info: Were text instances selected or filtered? -->
283
+ <!-- scope: telescope -->
284
+ not filtered
285
+
286
+
287
+ ### Structured Annotations
288
+
289
+ #### Additional Annotations?
290
+
291
+ <!-- quick -->
292
+ <!-- info: Does the dataset have additional annotations for each instance? -->
293
+ <!-- scope: telescope -->
294
+ found
295
+
296
+ #### Annotation Service?
297
+
298
+ <!-- info: Was an annotation service used? -->
299
+ <!-- scope: telescope -->
300
+ no
301
+
302
+ #### Annotation Values
303
+
304
+ <!-- info: Purpose and values for each annotation -->
305
+ <!-- scope: microscope -->
306
+ The input documents have section structure information.
307
+
308
+ #### Any Quality Control?
309
+
310
+ <!-- info: Quality control measures? -->
311
+ <!-- scope: telescope -->
312
+ validated by another rater
313
+
314
+ #### Quality Control Details
315
+
316
+ <!-- info: Describe the quality control measures that were taken. -->
317
+ <!-- scope: microscope -->
318
+ Bilingual annotators assessed the content overlap of source document and target summaries.
319
+
320
+
321
+ ### Consent
322
+
323
+ #### Any Consent Policy?
324
+
325
+ <!-- info: Was there a consent policy involved when gathering the data? -->
326
+ <!-- scope: telescope -->
327
+ no
328
+
329
+
330
+ ### Private Identifying Information (PII)
331
+
332
+ #### Contains PII?
333
+
334
+ <!-- quick -->
335
+ <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
336
+ <!-- scope: telescope -->
337
+ no PII
338
+
339
+
340
+ ### Maintenance
341
+
342
+ #### Any Maintenance Plan?
343
+
344
+ <!-- info: Does the original dataset have a maintenance plan? -->
345
+ <!-- scope: telescope -->
346
+ no
347
+
348
+
349
+
350
+ ## Broader Social Context
351
+
352
+ ### Previous Work on the Social Impact of the Dataset
353
+
354
+ #### Usage of Models based on the Data
355
+
356
+ <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
357
+ <!-- scope: telescope -->
358
+ no
359
+
360
+
361
+ ### Impact on Under-Served Communities
362
+
363
+ #### Addresses needs of underserved Communities?
364
+
365
+ <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
366
+ <!-- scope: telescope -->
367
+ no
368
+
369
+
370
+ ### Discussion of Biases
371
+
372
+ #### Any Documented Social Biases?
373
+
374
+ <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
375
+ <!-- scope: telescope -->
376
+ no
377
+
378
+
379
+
380
+ ## Considerations for Using the Data
381
+
382
+ ### PII Risks and Liability
383
+
384
+
385
+
386
+ ### Licenses
387
+
388
+ #### Copyright Restrictions on the Dataset
389
+
390
+ <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
391
+ <!-- scope: periscope -->
392
+ `public domain`
393
+
394
+ #### Copyright Restrictions on the Language Data
395
+
396
+ <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
397
+ <!-- scope: periscope -->
398
+ `public domain`
399
+
400
+
401
+ ### Known Technical Limitations
402
+
403
+
404
+
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