{"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/rouge-20-updated-and-improved-measures-for","title":"ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks","arxiv_id":"1803.01937","date":"2018-03-05","proceeding":null,"authors":["Kavita Ganesan"],"abstract":"Evaluation of summarization tasks is extremely crucial to determining the\nquality of machine generated summaries. Over the last decade, ROUGE has become\nthe standard automatic evaluation measure for evaluating summarization tasks.\nWhile ROUGE has been shown to be effective in capturing n-gram overlap between\nsystem and human composed summaries, there are several limitations with the\nexisting ROUGE measures in terms of capturing synonymous concepts and coverage\nof topics. Thus, often times ROUGE scores do not reflect the true quality of\nsummaries and prevents multi-faceted evaluation of summaries (i.e. by topics,\nby overall content coverage and etc). In this paper, we introduce ROUGE 2.0,\nwhich has several updated measures of ROUGE: ROUGE-N+Synonyms, ROUGE-Topic,\nROUGE-Topic+Synonyms, ROUGE-TopicUniq and ROUGE-TopicUniq+Synonyms; all of\nwhich are improvements over the core ROUGE measures.","url_abs":"http://arxiv.org/abs/1803.01937v1","url_pdf":"http://arxiv.org/pdf/1803.01937v1.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":"rouge-20-updated-and-improved-measures-for","repo_url":"https://github.com/chalothon/ROUGE2.0","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"rouge-20-updated-and-improved-measures-for","repo_url":"https://github.com/jacksonchen1998/Cold-Start-Reinforcement-Learning-with-Softmax-Policy-Gradient","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"rouge-20-updated-and-improved-measures-for","repo_url":"https://github.com/kavgan/ROUGE-2.0","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.01937","atlas_url":"https://app.syntology.ai/?focus=1803.01937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.01937"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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