ABSTRACT
We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.
- {Barzilay and Lee 2003} Barzilay, Regina and Lillian Lee. 2003. Learning to paraphrase: an unsupervised approach using multiple-sequence alignment. In NAACL '03: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pages 16--23, Morristown, NJ, USA. Association for Computational Linguistics. Google ScholarDigital Library
- {DeJong 1982} DeJong, Gerald F. 1982. An overview of the FRUMP system. In Lehnert, Wendy G. and Martin H. Ringle, editors, Strategies for Natural Language Processing, pages 149--176. Lawrence Erlbaum, Hillsdale, NJ.Google Scholar
- {Erkan and Radev 2004} Erkan, Günes and Dragomir R. Radev. 2004. Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res., 22(1):457--479. Google ScholarDigital Library
- {Finley and Harabagiu 2002} Finley, Sanda Harabagiu and Sanda M. Harabagiu. 2002. Generating single and multi-document summaries with gistexter. In Proceedings of the workshop on automatic summarization, pages 30--38.Google Scholar
- {Jing and McKeown 2000} Jing, Hongyan and Kathleen R. McKeown. 2000. Cut and paste based text summarization. In Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference, pages 178--185, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc. Google ScholarDigital Library
- {Lerman et al. 2009} Lerman, Kevin, Sasha Blair-Goldensohn, and Ryan Mcdonald. 2009. Sentiment summarization: Evaluating and learning user preferences. In 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL-09). Google ScholarDigital Library
- {Lin and Hovy 2003} Lin, Chin-Yew and Eduard Hovy. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proc. HLT-NAACL, page 8 pages. Google ScholarDigital Library
- {LIN 2004a} LIN, Chin-Yew. 2004a. Looking for a few good metrics: Rouge and its evaluation. proc. of the 4th NTCIR Workshops, 2004.Google Scholar
- {Lin 2004b} Lin, Chin-Yew. 2004b. Rouge: a package for automatic evaluation of summaries. In Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004), Barcelona, Spain.Google Scholar
- {Lu et al. 2009} Lu, Yue, ChengXiang Zhai, and Neel Sundaresan. 2009. Rated aspect summarization of short comments. In 18th International World Wide Web Conference (WWW 2009), April. Google ScholarDigital Library
- {Mihalcea and Tarau 2004} Mihalcea, R. and P. Tarau. 2004. TextRank: Bringing order into texts. In Proceedings of EMNLP-04and the 2004 Conference on Empirical Methods in Natural Language Processing, July.Google Scholar
- {Pang and Lee 2004} Pang, Bo and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the ACL, pages 271--278. Google ScholarDigital Library
- {Pang et al. 2002} Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 79--86. Google ScholarDigital Library
- {Radev and McKeown 1998} Radev, DR and K. McKeown. 1998. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469--500. Google ScholarDigital Library
- {Radev et al. 2000} Radev, Dragomir, Hongyan Jing, and Malgorzata Budzikowska. 2000. Centroid-based summarization of multiple documents: Sentence extraction, utility-based evaluation, and user studies. In In ANLP/NAACL Workshop on Summarization, pages 21--29. Google ScholarDigital Library
- {Radev et al. 2002} Radev, Dragomir R., Eduard Hovy, and Kathleen McKeown. 2002. Introduction to the special issue on summarization.Google Scholar
- {Saggion and Lapalme 2002} Saggion, Horacio and Guy Lapalme. 2002. Generating indicative-informative summaries with sumum. Computational Linguistics, 28(4):497--526. Google ScholarDigital Library
- {Snyder and Barzilay 2007} Snyder, Benjamin and Regina Barzilay. 2007. Multiple aspect ranking using the good grief algorithm. In In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL, pages 300--307.Google Scholar
- {Titov and Mcdonald 2008} Titov, Ivan and Ryan Mcdonald. 2008. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of ACL-08: HLT, pages 308--316, Columbus, Ohio, June. Association for Computational Linguistics.Google Scholar
- Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions
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