Abstract
We propose an unsupervised model to extract two types of summaries (positive, and negative) per document based on sentiment polarity. Our model builds a weighted polar digraph from the text, then evolves recursively until some desired properties converge. It can be seen as an enhanced variant of TextRank type algorithms working with non-polar text graphs. Each positive, negative, and objective opinion has some impact on the other if they are semantically related or placed close in the document.
Our experiments cover several interesting scenarios. In case of a one author news article, we notice a significant overlap between the anti-summary (focusing on negatively polarized sentences) and the the summary. For a transcript of a debate or a talk-show, an anti-summary represents the disagreement of the participants on stated topic(s) whereas the summary becomes the collection of positive feedbacks. In this case, the anti-summary tends to be disjoint from the regular summary. Overall, our experiments show that our model can be used with TextRank to enhance the quality of the extractive summarization process.
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References
Balahur, A., Kabadjov, M.A., Steinberger, J., Steinberger, R., Montoyo, A.: Summarizing opinions in blog threads. In: Kwong, O. (ed.) PACLIC, pp. 606–613. City University of Hong Kong Press (2009), http://dblp.uni-trier.de/db/conf/paclic/paclic2009.html#BalahurKSSM09
Beineke, P., Hastie, T., Manning, C., Vaithyanathan, S.: Exploring sentiment summarization. In: Qu, Y., Shanahan, J., Wiebe, J. (eds.) Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications. AAAI Press (2004), aAAI technical report SS-04-07
Erkan, G., Radev, D.R.: Lexrank: Graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res. 22(1), 457–479 (2004), http://dl.acm.org/citation.cfm?id=1622487.1622501
Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 2006), pp. 417–422 (2006)
Goldstein, J., Kantrowitz, M., Mittal, V., Carbonell, J.: Summarizing text documents: Sentence selection and evaluation metrics. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999, pp. 121–128. ACM, New York (1999), http://doi.acm.org/10.1145/312624.312665
Hovy, E., Lin, C.-Y.: Automated text summarization and the summarist system. In: Proceedings of a Workshop on TIPSTER 1998, Held at Baltimore, Maryland, October 13-15, pp. 97–214. Association for Computational Linguistics, Stroudsburg (1998), http://dx.doi.org/10.3115/1119089.1119121
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012), http://dx.doi.org/10.2200/S00416ED1V01Y201204HLT016
Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958), http://dx.doi.org/10.1147/rd.22.0159
Mao, Y., Lebanon, G.: Isotonic conditional random fields and local sentiment flow. In: Advances in Neural Information Processing Systems (2007)
Mihalcea, R., Tarau, P.: TextRank: Bringing order into texts. In: Proceedings of EMNLP 2004 and the 2004 Conference on Empirical Methods in Natural Language Processing (July 2004)
Mihalcea, R.: Graph-based ranking algorithms for sentence extraction, applied to text summarization. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, ACLdemo 2004 (2004)
Miller, G.A.: Wordnet: A lexical database for english. Communications of the ACM 38, 39–41 (1995)
Mishra, A., Bhattacharya, A.: Finding the bias and prestige of nodes in networks based on trust scores. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 567–576. ACM, New York (2011), http://doi.acm.org/10.1145/1963405.1963485
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Hamid, F., Tarau, P. (2015). Anti-Summaries: Enhancing Graph-Based Techniques for Summary Extraction with Sentiment Polarity. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_28
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DOI: https://doi.org/10.1007/978-3-319-18117-2_28
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