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{
"overview": {
"where": {
"has-leaderboard": "no",
"leaderboard-url": "N/A",
"leaderboard-description": "N/A",
"website": "None (See Repository)",
"data-url": "https://github.com/esdurmus/Wikilingua",
"paper-url": "https://www.aclweb.org/anthology/2020.findings-emnlp.360/",
"paper-bibtext": "@inproceedings{ladhak-etal-2020-wikilingua,\n title = \"{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization\",\n author = \"Ladhak, Faisal and\n Durmus, Esin and\n Cardie, Claire and\n McKeown, Kathleen\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.findings-emnlp.360\",\n doi = \"10.18653/v1/2020.findings-emnlp.360\",\n pages = \"4034--4048\",\n abstract = \"We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct cross-lingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.\",\n}",
"contact-name": "Faisal Ladhak, Esin Durmus",
"contact-email": "faisal@cs.columbia.edu, esdurmus@stanford.edu"
},
"languages": {
"is-multilingual": "yes",
"license": "cc-by-3.0: Creative Commons Attribution 3.0 Unported",
"task-other": "N/A",
"language-names": [
"English",
"Spanish, Castilian",
"Portuguese",
"French",
"German",
"Russian",
"Italian",
"Indonesian",
"Dutch, Flemish",
"Arabic",
"Chinese",
"Vietnamese",
"Thai",
"Japanese",
"Korean",
"Hindi",
"Czech",
"Turkish"
],
"intended-use": "The dataset was intended to serve as a large-scale, high-quality benchmark dataset for cross-lingual summarization.",
"license-other": "N/A",
"task": "Summarization",
"communicative": "Produce a high quality summary for the given input article.\n",
"language-dialects": "Dataset does not have multiple dialects per language.",
"language-speakers": "No information about the user demographic is available."
},
"credit": {
"organization-type": [
"academic"
],
"organization-names": "Columbia University",
"creators": "Faisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)",
"funding": "N/A",
"gem-added-by": "Jenny Chim (Queen Mary University of London), Faisal Ladhak (Columbia University)"
},
"structure": {
"structure-example": "{\n \"gem_id\": \"wikilingua_crosslingual-train-12345\",\n \"gem_parent_id\": \"wikilingua_crosslingual-train-12345\",\n \"source_language\": \"fr\",\n \"target_language\": \"de\",\n \"source\": \"Document in fr\",\n \"target\": \"Summary in de\",\n}",
"data-fields": "gem_id -- The id for the data instance.\nsource_language -- The language of the source article.\ntarget_language -- The language of the target summary.\nsource -- The source document.\n",
"structure-labels": "N/A",
"structure-description": "N/A",
"structure-splits-criteria": "The data was split to ensure the same document would appear in the same split across languages so as to ensure there's no leakage into the test set.",
"structure-outlier": "N/A",
"structure-splits": "The data is split into train/dev/test. In addition to the full test set, there's also a sampled version of the test set. "
}
},
"curation": {
"original": {
"rationale": "The dataset was created in order to enable new approaches for cross-lingual and multilingual summarization, which are currently understudied as well as open up inetersting new directions for research in summarization. E.g., exploration of multi-source cross-lingual architectures, i.e. models that can summarize from multiple source languages into a target language, building models that can summarize articles from any language to any other language for a given set of languages.",
"communicative": "Given an input article, produce a high quality summary of the article in the target language.",
"is-aggregated": "no",
"aggregated-sources": "N/A"
},
"language": {
"obtained": [
"Found"
],
"found": [
"Single website"
],
"crowdsourced": [],
"created": "N/A",
"machine-generated": "N/A",
"producers-description": "WikiHow, which is an online resource of how-to guides (written and reviewed by human authors) is used as the data source. ",
"topics": "The articles cover 19 broad categories including health, arts and entertainment, personal care and style, travel, education and communications, etc. The categories cover a broad set of genres and topics.",
"validated": "not validated",
"is-filtered": "not filtered",
"filtered-criteria": "N/A",
"pre-processed": "N/A"
},
"annotations": {
"origin": "none",
"rater-number": "N/A",
"rater-qualifications": "N/A",
"rater-training-num": "N/A",
"rater-test-num": "N/A",
"rater-annotation-service-bool": "no",
"rater-annotation-service": [],
"values": "N/A",
"quality-control": [],
"quality-control-details": "N/A"
},
"consent": {
"has-consent": "yes",
"consent-policy": "(1) Text Content. All text posted by Users to the Service is sub-licensed by wikiHow to other Users under a Creative Commons license as provided herein. The Creative Commons license allows such text content be used freely for non-commercial purposes, so long as it is used and attributed to the original author as specified under the terms of the license. Allowing free republication of our articles helps wikiHow achieve its mission by providing instruction on solving the problems of everyday life to more people for free. In order to support this goal, wikiHow hereby grants each User of the Service a license to all text content that Users contribute to the Service under the terms and conditions of a Creative Commons CC BY-NC-SA 3.0 License. Please be sure to read the terms of the license carefully. You continue to own all right, title, and interest in and to your User Content, and you are free to distribute it as you wish, whether for commercial or non-commercial purposes.",
"no-consent-justification": "N/A",
"consent-other": "The data is made freely available under the Creative Commons license, therefore there are no restrictions about downstream uses as long is it's for non-commercial purposes."
},
"pii": {
"has-pii": "no PII",
"no-pii-justification": "Only the article text and summaries were collected. No user information was retained in the dataset.",
"pii-categories": [],
"is-pii-identified": "N/A",
"pii-identified-method": "N/A",
"is-pii-replaced": "N/A",
"pii-replaced-method": "N/A"
},
"maintenance": {
"has-maintenance": "no",
"description": "N/A",
"contact": "N/A",
"contestation-mechanism": "N/A",
"contestation-link": "N/A",
"contestation-description": "N/A"
}
},
"gem": {
"rationale": {
"contribution": "This dataset provides a large-scale, high-quality resource for cross-lingual summarization in 18 languages, increasing the coverage of languages for the GEM summarization task. ",
"sole-task-dataset": "yes",
"sole-language-task-dataset": "yes",
"distinction-description": "XSum covers English news articles, and MLSum covers news articles in German and Spanish. \nIn contrast, this dataset has how-to articles in 18 languages, substantially increasing the languages covered. Moreover, it also provides a a different domain than the other two datasets.",
"model-ability": "The ability to generate quality summaries across multiple languages."
},
"curation": {
"has-additional-curation": "yes",
"modification-types": [
"other"
],
"modification-description": "Previous version had separate data loaders for each language. In this version, we've created a single monolingual data loader, which contains monolingual data in each of the 18 languages. In addition, we've also created a single cross-lingual data loader across all the language pairs in the dataset. ",
"has-additional-splits": "no",
"additional-splits-description": "N/A",
"additional-splits-capacicites": "N/A"
},
"starting": {}
},
"results": {
"results": {
"other-metrics-definitions": "N/A",
"has-previous-results": "no",
"current-evaluation": "N/A",
"previous-results": "N/A",
"model-abilities": "Ability to summarize content across different languages.",
"metrics": [
"ROUGE"
],
"original-evaluation": "ROUGE is used to measure content selection by comparing word overlap with reference summaries. In addition, the authors of the dataset also used human evaluation to evaluate content selection and fluency of the systems."
}
},
"considerations": {
"pii": {
"risks-description": "N/A"
},
"licenses": {
"dataset-restrictions-other": "N/A",
"data-copyright-other": "N/A",
"dataset-restrictions": [
"non-commercial use only"
],
"data-copyright": [
"non-commercial use only"
]
},
"limitations": {
"data-technical-limitations": "N/A",
"data-unsuited-applications": "N/A",
"data-discouraged-use": "N/A"
}
},
"context": {
"previous": {
"is-deployed": "yes - other datasets featuring the same task",
"described-risks": "N/A",
"changes-from-observation": "N/A"
},
"underserved": {
"helps-underserved": "no",
"underserved-description": "N/A"
},
"biases": {
"has-biases": "yes"
}
}
} |