ABSTRACT
With the abundance of reviews published on the Web about a given product, consumers are looking for ways to view major opinions that can be presented in a quick and succinct way. Reviews contain many different opinions, making the ability to show a diversified review summary that focus on coverage and diversity a major goal. Most review summarization work focuses on showing salient reviews as a summary which might ignore diversity in summaries. In this paper, we present a graph-based algorithm that is capable of producing extractive summaries that are both diversified from a sentiment point of view and topically well-covered. First, we use statistical measures to find topical words. Then we split the dataset based on the sentiment class of the reviews and perform the ranking on each sentiment graph. When compared with different baselines, our approach scores best in most ROUGE metrics. Specifically, our approach shows improvements of 3.9% in ROUGE-1 and 1.8% in ROUGE-L in comparison with the best competing baseline.
- Abdelghani Bellaachia and Mohammed Al-Dhelaan. 2015. Short text keyphrase extraction with hypergraphs. Progress in Artificial Intelligence 3, 2 (2015), 73--87.Google ScholarCross Ref
- Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual Web search engine. In WWW. 107--117. Google ScholarDigital Library
- Günes Erkan and Dragomir R. Radev. 2004. LexRank: graph-based lexical centrality as salience in summarization. Journal of Artificial Intelligence Research 22, 1 (Dec. 2004), 457--479. Google ScholarDigital Library
- Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. 2010. Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING '10). Association for Computational Linguistics, Stroudsburg, PA, USA, 340--348. http://dl.acm.org/citation.cfm?id=1873781.1873820 Google ScholarDigital Library
- Kavita Ganesan, ChengXiang Zhai, and Evelyne Viegas. 2012. Micropinion Generation: An Unsupervised Approach to Generating Ultra-concise Summaries of Opinions. In Proceedings of the 21st International Conference on World Wide Web (WWW '12). ACM, New York, NY, USA, 869--878. Google ScholarDigital Library
- Minqing Hu and Bing Liu. 2004. Mining and Summarizing Customer Reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04). ACM, New York, NY, USA, 168--177. Google ScholarDigital Library
- Ralf Krestel and Nima Dokoohaki. 2011. Diversifying Product Review Rankings: Getting the Full Picture. In Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01 (WI-IAT '11). IEEE Computer Society, Washington, DC, USA, 138--145. Google ScholarDigital Library
- Lun-Wei Ku, Li-Ying Lee, and Hsin-Hsi Chen. 2006. Opinion extraction, summarization and tracking in news and blog corpora. In In Proceedings of AAAI-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs.Google Scholar
- Chin-Yew Lin and Eduard Hovy. 2000. The Automated Acquisition of Topic Signatures for Text Summarization. In COLING. 495--501. Google ScholarDigital Library
- Chin-Yew Lin and Eduard Hovy. 2003. Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics. In NAACL. 71--78. Google ScholarDigital Library
- Bing Liu, Minqing Hu, and Junsheng Cheng. 2005. Opinion Observer: Analyzing and Comparing Opinions on the Web. In Proceedings of the 14th International Conference on World Wide Web (WWW '05). ACM, New York, NY, USA, 342--351. Google ScholarDigital Library
- Yue Lu, ChengXiang Zhai, and Neel Sundaresan. 2009. Rated Aspect Summarization of Short Comments. In Proceedings of the 18th International Conference on World Wide Web (WWW '09). ACM, New York, NY, USA, 131--140. Google ScholarDigital Library
- Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit. In Association for Computational Linguistics (ACL) System Demonstrations. 55--60. http://www.aclweb.org/anthology/P/P14/P14-5010Google Scholar
- Rada Mihalcea and Paul Tarau. 2004. TextRank: Bringing Order into Texts. In EMNLP, Dekang Lin and Dekai Wu (Eds.). Barcelona, Spain, 404--411.Google Scholar
- Natwar Modani, Elham Khabiri, Harini Srinivasan, and James Caverlee. 2015. Creating Diverse Product Review Summaries: A Graph Approach. Springer International Publishing, Cham, 169--184. Google ScholarDigital Library
- Thanh-Son Nguyen, Hady W. Lauw, and Panayiotis Tsaparas. 2015. Review Synthesis for Micro-Review Summarization. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (WSDM '15). ACM, New York, NY, USA, 169--178. Google ScholarDigital Library
- Hitoshi Nishikawa, Takaaki Hasegawa, Yoshihiro Matsuo, and Genichiro Kikui. 2010. Optimizing Informativeness and Readability for Sentiment Summarization. In Proceedings of the ACL 2010 Conference Short Papers. Association for Computational Linguistics, 325--330. http://aclweb.org/anthology/P10-2060 Google ScholarDigital Library
- Michael Paul, ChengXiang Zhai, and Roxana Girju. 2010. Summarizing Contrastive Viewpoints in Opinionated Text. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 66--76. http://aclweb.org/anthology/D10-1007 Google ScholarDigital Library
- Zhaochun Ren and Maarten de Rijke. 2015. Summarizing Contrastive Themes via Hierarchical Non-Parametric Processes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15). ACM, New York, NY, USA, 93--102. Google ScholarDigital Library
- Yohei Seki, Koji Eguchi, Noriko Kando, and Masaki Aono. 2006. Opinion-focused summarization and its analysis at DUC 2006. In In Proceedings of the Document Understanding Conference.Google Scholar
- Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Seattle, Washington, USA, 1631-1642. http://www.aclweb.org/anthology/D13-1170Google Scholar
- Veselin Stoyanov and Claire Cardie. 2006. Partially supervised coreference resolution for opinion summarization through structured rule learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 336--344. Google ScholarDigital Library
- Lucy Vanderwende, Hisami Suzuki, Chris Brockett, and Ani Nenkova. 2007. Beyond SumBasic: Task-focused Summarization with Sentence Simplification and Lexical Expansion. Inf. Process. Manage. 43, 6 (Nov. 2007), 1606--1618. Google ScholarDigital Library
- Lu Wang, Hema Raghavan, Claire Cardie, and Vittorio Castelli. 2014. Query-Focused Opinion Summarization for User-Generated Content. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. Dublin City University and Association for Computational Linguistics, 1660--1669. http://aclweb.org/anthology/C14-1157Google Scholar
- Linhong Zhu, Sheng Gao, Sinno Jialin Pan, Haizhou Li, Dingxiong Deng, and Cyrus Shahabi. 2013. Graph-based Informative-sentence Selection for Opinion Summarization. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '13). ACM, New York, NY, USA, 408--412. Google ScholarDigital Library
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