{"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/unsupervised-abstractive-meeting","title":"Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization","arxiv_id":"1805.05271","date":"2018-05-14","proceeding":"ACL 2018 7","authors":["Guokan Shang","Wensi Ding","Zekun Zhang","Antoine Jean-Pierre Tixier","Polykarpos Meladianos","Michalis Vazirgiannis","Jean-Pierre Lorré"],"abstract":"We introduce a novel graph-based framework for abstractive meeting speech\nsummarization that is fully unsupervised and does not rely on any annotations.\nOur work combines the strengths of multiple recent approaches while addressing\ntheir weaknesses. Moreover, we leverage recent advances in word embeddings and\ngraph degeneracy applied to NLP to take exterior semantic knowledge into\naccount, and to design custom diversity and informativeness measures.\nExperiments on the AMI and ICSI corpus show that our system improves on the\nstate-of-the-art. Code and data are publicly available, and our system can be\ninteractively tested.","url_abs":"http://arxiv.org/abs/1805.05271v2","url_pdf":"http://arxiv.org/pdf/1805.05271v2.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":"unsupervised-abstractive-meeting","repo_url":"https://bitbucket.org/dascim/acl2018_abssumm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"unsupervised-abstractive-meeting","repo_url":"https://github.com/Tixierae/gow_tools","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"unsupervised-abstractive-meeting","repo_url":"https://github.com/xcfcode/Summarization-Papers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"unsupervised-abstractive-meeting","repo_url":"https://github.com/bearblog/CoreRank","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"abstractive-dialogue-summarization","task_name":"Abstractive Dialogue Summarization"},{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"dialogue-understanding","task_name":"Dialogue Understanding"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"informativeness","task_name":"Informativeness"},{"task_slug":"meeting-summarization","task_name":"Meeting Summarization"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-compression","task_name":"Sentence Compression"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"uns","method_name":"UNS"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/meeting-summarization-on-ami-meeting-corpus","task":"Meeting Summarization","dataset":"AMI Meeting Corpus","model":"UNS","rank_in_archive_order":1,"of":1,"metrics":{"ROUGE-1 F1":"37.53"},"uses_additional_data":false},{"leaderboard":"/sota/meeting-summarization-on-icsi-meeting-corpus","task":"Meeting Summarization","dataset":"ICSI Meeting Corpus","model":"UNS","rank_in_archive_order":1,"of":1,"metrics":{"ROUGE-1 F1":"34.11"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.05271","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}