{"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/ordersum-semantic-sentence-ordering-for","title":"OrderSum: Semantic Sentence Ordering for Extractive Summarization","arxiv_id":"2502.16180","date":"2025-02-22","proceeding":null,"authors":["Taewan Kwon","Sangyong Lee"],"abstract":"There are two main approaches to recent extractive summarization: the sentence-level framework, which selects sentences to include in a summary individually, and the summary-level framework, which generates multiple candidate summaries and ranks them. Previous work in both frameworks has primarily focused on improving which sentences in a document should be included in the summary. However, the sentence order of extractive summaries, which is critical for the quality of a summary, remains underexplored. In this paper, we introduce OrderSum, a novel extractive summarization model that semantically orders sentences within an extractive summary. OrderSum proposes a new representation method to incorporate the sentence order into the embedding of the extractive summary, and an objective function to train the model to identify which extractive summary has a better sentence order in the semantic space. Extensive experimental results demonstrate that OrderSum obtains state-of-the-art performance in both sentence inclusion and sentence order for extractive summarization. In particular, OrderSum achieves a ROUGE-L score of 30.52 on CNN/DailyMail, outperforming the previous state-of-the-art model by a large margin of 2.54.","url_abs":"https://arxiv.org/abs/2502.16180v1","url_pdf":"https://arxiv.org/pdf/2502.16180v1.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":"ordersum-semantic-sentence-ordering-for","repo_url":"https://github.com/Espresso-AI/OrderSum","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"extractive-summarization","task_name":"Extractive Summarization"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-ordering","task_name":"Sentence Ordering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}