{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:11:47Z","timestamp":1775283107674,"version":"3.50.1"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2006,7,1]],"date-time":"2006-07-01T00:00:00Z","timestamp":1151712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Speech Lang. Process."],"published-print":{"date-parts":[[2006,7]]},"abstract":"<jats:p>This article presents a multidocument, multilingual, theme-based summarization system based on modeling text cohesion (story flow). Conventional extractive summarization systems which pick out salient sentences to include in a summary often disregard any flow or sequence that might exist between these sentences. We argue that such inherent text cohesion exists and is (1) specific to a particular story and (2) specific to a particular language. Documents within the same story, and in the same language, share a common story flow, and this flow differs across stories, and across languages. We propose using Hidden Markov Models (HMMs) as story models. An unsupervised segmental K-means method is used to iteratively cluster multiple documents into different topics (stories) and learn the parameters of parallel Hidden Markov Story Models (HMSM), one for each story. We compare story models within and across stories and within and across languages (English and Chinese). The experimental results support our \u201cone story, one flow\u201d and \u201cone language, one flow\u201d hypotheses. We also propose a Na\u00efve Bayes classifier for document summarization. The performance of our summarizer is superior to conventional methods that do not incorporate text cohesion information. Our HMSM method also provides a simple way to compile a single metasummary for multiple documents from individual summaries via state labeled sentences.<\/jats:p>","DOI":"10.1145\/1149290.1151099","type":"journal-article","created":{"date-parts":[[2006,10,18]],"date-time":"2006-10-18T18:11:32Z","timestamp":1161195092000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["One story, one flow"],"prefix":"10.1145","volume":"3","author":[{"given":"Pascale","family":"Fung","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Grace","family":"Ngai","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2006,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 1st Human Language Technology Conference","author":"Barzilay R.","unstructured":"Barzilay , R. , Elhadad , N. , and Mckeown , K . 2001. Sentence ordering in multi-document summarization . In Proceedings of the 1st Human Language Technology Conference . San Diego, CA, 149--156. 10.3115\/1072133.1072217 Barzilay, R., Elhadad, N., and Mckeown, K. 2001. Sentence ordering in multi-document summarization. In Proceedings of the 1st Human Language Technology Conference. San Diego, CA, 149--156. 10.3115\/1072133.1072217"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of HLT-NAACL","author":"Barzilay R.","unstructured":"Barzilay , R. and Lee , L . 2004. Catching the drift: Probabilistic content models, with applications to generation and summarization . In Proceedings of HLT-NAACL . Boston, MA, 113--120. Barzilay, R. and Lee, L. 2004. Catching the drift: Probabilistic content models, with applications to generation and summarization. In Proceedings of HLT-NAACL. Boston, MA, 113--120."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 2nd Conference on Applied Natural Language Processing","author":"Church K.","year":"1988","unstructured":"Church , K. 1988 . A stochastic parts program and noun phrase parser for unrestricted text . In Proceedings of the 2nd Conference on Applied Natural Language Processing . Austin, TX, 136--143. 10.3115\/974235.974260 Church, K. 1988. A stochastic parts program and noun phrase parser for unrestricted text. In Proceedings of the 2nd Conference on Applied Natural Language Processing. Austin, TX, 136--143. 10.3115\/974235.974260"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 35th Conference of the Association of Computational Linguistics","author":"Dagan I.","unstructured":"Dagan , I. , Lee , L. , and Pereira , F . 1997. Similarity-based methods for word sense disambiguation . In Proceedings of the 35th Conference of the Association of Computational Linguistics , Madrid, Spain, 56--63. 10.3115\/976909.979625 Dagan, I., Lee, L., and Pereira, F. 1997. Similarity-based methods for word sense disambiguation. In Proceedings of the 35th Conference of the Association of Computational Linguistics, Madrid, Spain, 56--63. 10.3115\/976909.979625"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 37th Conference of the Association of Computational Linguistics","author":"Fung P.","unstructured":"Fung , P. , Liu , X. , and Cheung , C. S . 1999. Mixed-language query disambiguation . In Proceedings of the 37th Conference of the Association of Computational Linguistics , College Park, MD. 10.3115\/1034678.1034732 Fung, P., Liu, X., and Cheung, C. S. 1999. Mixed-language query disambiguation. 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