{"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/a-supervised-approach-to-extractive","title":"A Supervised Approach to Extractive Summarisation of Scientific Papers","arxiv_id":"1706.03946","date":"2017-06-13","proceeding":"CONLL 2017 8","authors":["Ed Collins","Isabelle Augenstein","Sebastian Riedel"],"abstract":"Automatic summarisation is a popular approach to reduce a document to its\nmain arguments. Recent research in the area has focused on neural approaches to\nsummarisation, which can be very data-hungry. However, few large datasets exist\nand none for the traditionally popular domain of scientific publications, which\nopens up challenging research avenues centered on encoding large, complex\ndocuments. In this paper, we introduce a new dataset for summarisation of\ncomputer science publications by exploiting a large resource of author provided\nsummaries and show straightforward ways of extending it further. We develop\nmodels on the dataset making use of both neural sentence encoding and\ntraditionally used summarisation features and show that models which encode\nsentences as well as their local and global context perform best, significantly\noutperforming well-established baseline methods.","url_abs":"http://arxiv.org/abs/1706.03946v1","url_pdf":"http://arxiv.org/pdf/1706.03946v1.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":"a-supervised-approach-to-extractive","repo_url":"https://github.com/EdCo95/scientific-paper-summarisation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"a-supervised-approach-to-extractive","repo_url":"https://github.com/jananiarunachalam/Research-Paper-Summarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[{"slug":"cspubsum","name":"CSPubSum","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.03946","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}