{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-xscience-a-large-scale-dataset-for","title":"Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles","arxiv_id":"2010.14235","date":"2020-10-27","proceeding":"EMNLP 2020 11","authors":["Yao Lu","Yue Dong","Laurent Charlin"],"abstract":"Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results---using several state-of-the-art models trained on the Multi-XScience dataset---reveal that Multi-XScience is well suited for abstractive models.","url_abs":"https://arxiv.org/abs/2010.14235v1","url_pdf":"https://arxiv.org/pdf/2010.14235v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-xscience-a-large-scale-dataset-for","repo_url":"https://github.com/yaolu/Multi-XScience","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"descriptive","task_name":"Descriptive"},{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"extreme-summarization","task_name":"Extreme Summarization"},{"task_slug":"multi-document-summarization","task_name":"Multi-Document Summarization"}],"methods":[],"datasets_introduced":[{"slug":"multi-xscience","name":"Multi-XScience","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.14235","atlas_url":"https://app.syntology.ai/?focus=2010.14235","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}