Abstract
Product reviews play an influential role for the e-commerce websites, as consumers leverage them during the purchase decision process. However, the volume of such reviews can be overwhelming for a web user to comprehend the gist of overall information communicated by other consumers. In this paper, we address the problem of summarizing user contributed product reviews, having certain properties that differentiate them significantly from summarizing of traditional text articles. We propose suitable summarization algorithms that capture useful information with minimum redundancy and maximum information. We present a graph based formulation using a fast and scalable greedy algorithm for the review summarization problem. Our approach provides a rich model that makes certain sentences more rewarding based on their properties, in addition to their relation to the other reviews. We evaluate and show that our proposed algorithm outperforms other state-of-the-art summarization algorithms with significance level of 0.01 using automatic evaluation.
Keywords
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Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boomboxes and blenders: domain adaptation for sentiment classification. In: ACL, pp. 187–205 (2007)
Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 335–336. ACM, New York (1998). http://doi.acm.org/10.1145/290941.291025
Ganesan, K., Zhai, C., Han, J.: Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 340–348. Association for Computational Linguistics (2010)
Ganesan, K., Zhai, C., Viegas, E.: Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. In: Proceedings of the 21st International Conference on World Wide Web, pp. 869–878. ACM (2012)
Hassel, M.: Evaluation of automatic text summarization, pp. 1–75. Licentiate Thesis, Stockholm, Sweden (2004)
Hovy, E., Lin, C.Y.: Automated text summarization in summarist (1999)
Hsu, C.F., Khabiri, E., Caverlee, J.: Ranking comments on the social web. In: CSE vol. 4, pp. 90–97 (2009)
Khabiri, E., Caverlee, J., Hsu, C.F.: Summarizing user-contributed comments. In: ICWSM (2011)
Khabiri, E., Hsu, C.F., Caverlee, J.: Analyzing and predicting community preference of socially generated metadata: a case study on comments in the digg community. In: ICWSM (2009)
Khuller, S., Moss, A., Noar, J.: The budgeted maximum coverage problem. Inf. Process. Lett. 70(1), 39–45 (1999)
Kleinberg, J.M.: Hubs, authorities, and communities. ACM Comput. Surv. 31(4es), December 1999. http://dx.doi.org/10.1145/345966.345982
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Proceedings of the ACL-04 Workshop on Text Summarization Branches Out, pp. 74–81 (2004)
Louis, A., Nenkova, A.: Automatically evaluating content selection in summarization without human models. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, vol. 1, pp. 306–314. Association for Computational Linguistics, Stroudsburg, PA, USA (2009). http://dl.acm.org/citation.cfm?id=1699510.1699550
Mei, Q., Guo, J., Radev, D.: Divrank: the interplay of prestige and diversity in information networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1009–1018. ACM (2010)
Mihalcea, R.: Language independent extractive summarization. In: Proceedings of the ACL 2005 on Interactive Poster and Demonstration Sessions, pp. 49–52. Association for Computational Linguistics, Stroudsburg (2005). http://dx.doi.org/10.3115/1225753.1225766
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998). http://citeseer.ist.psu.edu/page98pagerank.html
Pemantle, R.: Vertex-reinforced random walk. Probab. Theory Relat. Fields 92(1), 117–136 (1992)
Radev, D.R.: Experiments in single and multidocument summarization using mead. In: First Document Understanding Conference (2001)
Radev, D.R.: Lexrank: graph-based centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)
Tong, H., He, J., Wen, Z., Konuru, R., Lin, C.Y.: Diversified ranking on large graphs: an optimization viewpoint. KDD 11, 1028–1036 (2011)
Wu, J., Xu, B., Li, S.: An unsupervised approach to rank product reviews. In: FSKD, pp. 1769–1772 (2011)
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Modani, N., Khabiri, E., Srinivasan, H., Caverlee, J. (2015). Creating Diverse Product Review Summaries: A Graph Approach. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_12
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DOI: https://doi.org/10.1007/978-3-319-26190-4_12
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