{"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/dialsumm-a-real-life-scenario-dialogue","title":"DialogSum: A Real-Life Scenario Dialogue Summarization Dataset","arxiv_id":"2105.06762","date":"2021-05-14","proceeding":"Findings (ACL) 2021 8","authors":["Yulong Chen","Yang Liu","Liang Chen","Yue Zhang"],"abstract":"Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of real-life scenarios including customer service management and medication tracking. To this end, we propose DialogSum, a large-scale labeled dialogue summarization dataset. We conduct empirical analysis on DialogSum using state-of-the-art neural summarizers. Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.","url_abs":"https://arxiv.org/abs/2105.06762v4","url_pdf":"https://arxiv.org/pdf/2105.06762v4.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":"dialsumm-a-real-life-scenario-dialogue","repo_url":"https://github.com/cylnlp/DialogSum","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"abstractive-dialogue-summarization","task_name":"Abstractive Dialogue Summarization"},{"task_slug":"common-sense-reasoning","task_name":"Common Sense Reasoning"},{"task_slug":"management","task_name":"Management"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[{"method_slug":null,"method_name":null}],"datasets_introduced":[{"slug":"dialogsum","name":"DialogSum","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2105.06762","atlas_url":"https://app.syntology.ai/?focus=2105.06762","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}