{"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/extractive-approach-for-text-summarisation","title":"Extractive approach for text summarisation using graphs","arxiv_id":"2106.10955","date":"2021-06-21","proceeding":null,"authors":["Kastriot Kadriu","Milenko Obradovic"],"abstract":"Natural language processing is an important discipline with the aim of understanding text by its digital representation, that due to the diverse way we write and speak, is often not accurate enough. Our paper explores different graph-related algorithms that can be used in solving the text summarization problem using an extractive approach. We consider two metrics: sentence overlap and edit distance for measuring sentence similarity.","url_abs":"https://arxiv.org/abs/2106.10955v1","url_pdf":"https://arxiv.org/pdf/2106.10955v1.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":"extractive-approach-for-text-summarisation","repo_url":"https://github.com/tot98git/text-summ","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-similarity","task_name":"Sentence Similarity"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}