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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6562))

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Abstract

In this paper we address the question of whether “very positive” or “very negative” sentences from the perspective of sentiment analysis are “good” summary sentences from the perspective of text summarisation. We operationalise the concepts of very positive and very negative sentences by using the output of a sentiment analyser and evaluate how good a sentence is for summarisation by making use of standard text summarisation metrics and a corpus annotated for both salience and sentiment. In addition, we design and execute a statistical test to evaluate the aforementioned hypothesis. We conclude that the hypothesis does not hold, at least not based on our corpus data, and argue that summarising sentiment and summarising text are two different tasks which should be treated separately.

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References

  1. Balahur, A., Lloret, E., Ferrández, O., Montoyo, A., Palomar, M., Muñoz, R.: The DLSIUAES team’s participation in the TAC 2008 tracks. In: National Institute of Standards and Technology [22]

    Google Scholar 

  2. Balahur, A., Lloret, E., Boldrini, E., Montoyo, A., Palomar, M., Martínez-Barco, P.: Summarizing threads in blogs using opinion polarity. In: Proceeding of the Workshop on Events in Emerging Text Types at RANLP, Borovetz, Bulgaria (September 2009)

    Google Scholar 

  3. Balahur, A., Steinberger, R., van der Goot, E., Pouliquen, B.: Opinion mining from newspaper quotations. In: Proceedings of the Workshop on Intelligent Analysis and Processing of Web News Content at the IEEE / WIC / ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT) (2009)

    Google Scholar 

  4. Bossard, A., Généreux, M., Poibeau, T.: Description of the LIPN systems at TAC 2008: Summarizing information and opinions. In: National Institute of Standards and Technology [22]

    Google Scholar 

  5. Cerini, S., Compagnoni, V., Demontis, A., Formentelli, M., Gandini, G.: Micro-WNOp: A gold standard for the evaluation of automatically compiled lexical resources for opinion mining. In: Sansò, A. (ed.) Language Resources and Linguistic Theory: Typology, Second Language Acquisition, English Linguistics, Franco Angeli, Milano, IT (2007)

    Google Scholar 

  6. Chaovalit, P., Zhou, L.: Movie review mining: a comparison between supervised and unsupervised classification approaches. In: Proceeding of HICSS 2005, the 38th Hawaii International Conference on System Sciences (2005)

    Google Scholar 

  7. Conroy, J., Schlesinger, S.: Classy at TAC 2008 metrics. In: National Institute of Standards and Technology [22]

    Google Scholar 

  8. Cruz, F., Troyani, J., Ortega, J., Enríquez, F.: The Italica system at TAC 2008 opinion summarization task. In: National Institute of Standards and Technology [22]

    Google Scholar 

  9. Dave, K., Lawrence, S., Pennock, D.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceeding of the World Wide Web Conference (2003)

    Google Scholar 

  10. Riloff, E., Wiebe, J., Phillips, W.: Exploiting subjectivity classification to improve information extraction. In: Proceedings of the 20th National Conference on Artificial Intelligence (AAAI) (2005)

    Google Scholar 

  11. Erkan, G., Radev, D.R.: LexRank: Graph-based centrality as salience in text summarization. Journal of Artificial Intelligence Research, JAIR (2004)

    Google Scholar 

  12. Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available resource for opinion mining. In: Proceeding of the 6th International Conference on Language Resources and Evaluation, Italy (May 2006)

    Google Scholar 

  13. He, T., Chen, J., Gui, Z., Li, F.: CCNU at TAC 2008: Proceeding on using semantic method for automated summarization yield. In: National Institute of Standards and Technology [22]

    Google Scholar 

  14. Hovy, E.H.: Automated text summarization. In: Mitkov, R. (ed.) The Oxford Handbook of Computational Linguistics, pp. 583–598. Oxford University Press, Oxford (2005)

    Google Scholar 

  15. Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceeding of the National Conference on Artificial Intelligence (AAAI) (2004)

    Google Scholar 

  16. Kabadjov, M.A., Steinberger, J., Pouliquen, B., Steinberger, R., Poesio, M.: Multilingual statistical news summarisation: Preliminary experiments with english. In: Proceedings of the Workshop on Intelligent Analysis and Processing of Web News Content at the IEEE / WIC / ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT) (2009)

    Google Scholar 

  17. Kim, S., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the International Conference on Computational Linguistics (COLING) (2004)

    Google Scholar 

  18. Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, pp. 68–73 (1995)

    Google Scholar 

  19. Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Proceedings of the Workshop on Text Summarization Branches Out, Barcelona, Spain (2004)

    Google Scholar 

  20. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)

    Article  Google Scholar 

  21. Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceeding of the Conference on Empirical Methods in Natural Language Processing (2003)

    Google Scholar 

  22. National Institute of Standards and Technology (eds.): Proceeding of the Text Analysis Conference. Gaithersburg, MD (November 2008)

    Google Scholar 

  23. Stoyanov, V., Cardie, C.: Toward opinion summarization: Linking the sources. In: Proceedings of the COLING-ACL Workshop on Sentiment and Subjectivity in Text.Association for Computational Linguistics, Sydney (July 2006)

    Google Scholar 

  24. Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of wordnet. In: Proceeding of the 4th International Conference on Language Resources and Evaluation, Lisbon, Portugal, pp. 1083–1086 (May 2004)

    Google Scholar 

  25. Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceeding of the Annual Meeting of the Association for Computational Linguistics (ACL) (2002)

    Google Scholar 

  26. Varma, V., Pingali, P., Katragadda, R., Krisha, S., Ganesh, S., Sarvabhotla, K., Garapati, H., Gopisetty, H., Reddy, V., Bysani, P., Bharadwaj, R.: IIT Hyderabad at TAC 2008. In: National Institute of Standards and Technology [22]

    Google Scholar 

  27. Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? finding strong and weak opinion clauses. In: Proceeding of the National Conference on Artificial Intelligence (AAAI) (2004)

    Google Scholar 

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Kabadjov, M., Balahur, A., Boldrini, E. (2011). Sentiment Intensity: Is It a Good Summary Indicator?. In: Vetulani, Z. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2009. Lecture Notes in Computer Science(), vol 6562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20095-3_19

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  • DOI: https://doi.org/10.1007/978-3-642-20095-3_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20094-6

  • Online ISBN: 978-3-642-20095-3

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