{"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/scisummnet-a-large-annotated-dataset-and","title":"ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks","arxiv_id":"1909.01716","date":"2019-09-04","proceeding":null,"authors":["Michihiro Yasunaga","Jungo Kasai","Rui Zhang","Alexander R. Fabbri","Irene Li","Dan Friedman","Dragomir R. Radev"],"abstract":"Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community. This paper provides novel solutions to these two challenges. We 1) develop and release the first large-scale manually-annotated corpus for scientific papers (on computational linguistics) by enabling faster annotation, and 2) propose summarization methods that integrate the authors' original highlights (abstract) and the article's actual impacts on the community (citations), to create comprehensive, hybrid summaries. We conduct experiments to demonstrate the efficacy of our corpus in training data-driven models for scientific paper summarization and the advantage of our hybrid summaries over abstracts and traditional citation-based summaries. Our large annotated corpus and hybrid methods provide a new framework for scientific paper summarization research.","url_abs":"https://arxiv.org/abs/1909.01716v3","url_pdf":"https://arxiv.org/pdf/1909.01716v3.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":"scisummnet-a-large-annotated-dataset-and","repo_url":"https://github.com/WING-NUS/scisumm-corpus","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"scientific-article-summarization","task_name":"Scientific Document Summarization"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[],"datasets_introduced":[{"slug":"scisummnet","name":"ScisummNet","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/scientific-article-summarization-on-cl","task":"Scientific Document Summarization","dataset":"CL-SciSumm","model":"GCN Hybrid","rank_in_archive_order":1,"of":1,"metrics":{"ROUGE-2":"33.88"},"uses_additional_data":false},{"leaderboard":"/sota/text-summarization-on-cl-scisumm","task":"Text Summarization","dataset":"CL-SciSumm","model":"GCN Hybrid","rank_in_archive_order":1,"of":1,"metrics":{"ROUGE-2":"33.88"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1909.01716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.01716"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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