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Analog Textual Entailment and Spectral Clustering (ATESC) Based Summarization

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7678)

Abstract

In the domain of single document and automatic extractive text summarization, a recent approach Logical TextTiling (LTT) has been proposed [1]. In-depth analysis has revealed that LTTs performance is limited by unfair entailment calculation, weak segmentation and assignment of equal importance to each segment produced. It seems that because of these drawbacks, the summary produced from experimentation on articles collected from New York Times website has been of poor/inferior quality. To overcome these limitations, the present paper proposes a novel technique called ATESC(Analog Textual Entailment and Spectral Clustering) which employs the use of analog entailment values in the range [0,1], segmentation using Normalized Spectral Clustering, and assignment of relative importance to the produced segments based on the scores of constituent sentences. At the end, a comparative study of results of LTT and ATESC is carried out. It shows that ATESC produces better quality of summaries in most of the cases tested experimentally.

Keywords

  • Textual Entailment
  • Spectral Clustering
  • Text Segmentation

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References

  1. Tatar, D., Tamaianu-Morita, E., Mihis, A., Lupsa, D.: Summarization by Logic Segmentation and text Entailment. In: The Proceedings of Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2008), Haifa, Israel, February 17-23, pp. 15–26 (2008)

    Google Scholar 

  2. Jones, K.S.: Automatic summarizing: The state of the art. Information Processing & Management 43(6), 1449–1481 (2007)

    CrossRef  Google Scholar 

  3. Iftene, A.: Thesis on AI, Textual Entailment, TR 09-02, University “Alexandru Ioan Cuza” of Iasi, Faculty of Computer Science (October 2009)

    Google Scholar 

  4. Delmonte, R.: Venses, http://project.cgm.unive.it/venses_en.html

  5. Ng, A., Jordan, M.I., Weiss, Y.: On Spectral Clustering: Analysis and an algorithm. In: Dietterich, T., Becker, S., Ghahramani, Z. (eds.) The Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-8, pp. 849–856 (2001)

    Google Scholar 

  6. Luxburg, U.V.: A Tutorial on Spectral Clustering. Journal Statistics and Computing 17(4), 1–32 (2007)

    Google Scholar 

  7. Classic Essays, http://grammar.about.com/od/classicessays/CLASSIC_ESSAYS.html , News Articles, http://www.nytimes.com/

  8. Radev, D.R., Hovy, E., McKeown, K.: Introduction to the Special issue on Summarization. Journal Computational Linguistics 28(4), 399–408 (2002)

    CrossRef  Google Scholar 

  9. Microsoft Auto Summarizer 2007 is used to identify the key points in the document and create a summary, http://office.microsoft.com/en-us/word-help/automatically-summarize-a-document-HA010255206.aspx

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© 2012 Springer-Verlag Berlin Heidelberg

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Gupta, A., Kathuria, M., Singh, A., Sachdeva, A., Bhati, S. (2012). Analog Textual Entailment and Spectral Clustering (ATESC) Based Summarization. In: Srinivasa, S., Bhatnagar, V. (eds) Big Data Analytics. BDA 2012. Lecture Notes in Computer Science, vol 7678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35542-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-35542-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35541-7

  • Online ISBN: 978-3-642-35542-4

  • eBook Packages: Computer ScienceComputer Science (R0)