{"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/mind-the-gap-injecting-commonsense-knowledge","title":"Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization","arxiv_id":"2209.00930","date":"2022-09-02","proceeding":"COLING 2022 10","authors":["Seungone Kim","Se June Joo","Hyungjoo Chae","Chaehyeong Kim","Seung-won Hwang","Jinyoung Yeo"],"abstract":"In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We present SICK, a framework that uses commonsense inferences as additional context. Compared to previous work that solely relies on the input dialogue, SICK uses an external knowledge model to generate a rich set of commonsense inferences and selects the most probable one with a similarity-based selection method. Built upon SICK, SICK++ utilizes commonsense as supervision, where the task of generating commonsense inferences is added upon summarizing the dialogue in a multi-task learning setting. Experimental results show that with injected commonsense knowledge, our framework generates more informative and consistent summaries than existing methods.","url_abs":"https://arxiv.org/abs/2209.00930v1","url_pdf":"https://arxiv.org/pdf/2209.00930v1.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":"mind-the-gap-injecting-commonsense-knowledge","repo_url":"https://github.com/SeungoneKim/SICK_Summarization","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"abstractive-dialogue-summarization","task_name":"Abstractive Dialogue Summarization"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-summarization-on-dialogsum","task":"Text Summarization","dataset":"DialogSum","model":"SICK","rank_in_archive_order":4,"of":4,"metrics":{"BertScore":"71.30","Rouge1":"46.26","Rouge2":"20.95","RougeL":"41.05"},"uses_additional_data":false},{"leaderboard":"/sota/text-summarization-on-samsum-corpus","task":"Text Summarization","dataset":"SAMSum","model":"SICK","rank_in_archive_order":6,"of":12,"metrics":{"BertScoreF1":"71.92","ROUGE-1":"53.73","ROUGE-2":"28.81","ROUGE-L":"49.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2209.00930","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}