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Graph Based Single Document Summarization

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6411))

Abstract

E-Learning aims at defining education to be made as anytime, anywhere and anybody entity. Usability can be increased by incorporating summarization in E-learning context. The aim of the text summarization is to select the most important information from an abundance of text. This paper investigates a new approach for single document summarization based on graph traversal technique with constraint to improve cohesion. The selection of features plays a vital role in the sentence extraction. By considering both the structured and the unstructured features, better summary can be generated.

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References

  1. Chuang, T.W., Yang, J.: Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms. In: Proceedings of the ACL 2004 Workshop, Barcelona (2004)

    Google Scholar 

  2. Luhn: The Automatic Creation of Literature Abstracts. IBM Journal of R& D (2) (1958)

    Google Scholar 

  3. Edmundson, H.P.: New Methods in Automatic Abstraction. ACM Journal (1969)

    Google Scholar 

  4. Kupiec, J., Pederson, J., Chen, F.: A Trainable Document Summarizer. In: Proceedings of the 18 th Annual International ACM SIGIR Conference on R&D in Information Retrieval, Seattle, Washington, pp. 68–73 (1995)

    Google Scholar 

  5. Baxendale, P.B.: Machine-Made index for Technical Literature: An Experiment. IBM Journal of R&D 2(4) (1958)

    Google Scholar 

  6. Radev, D.R., Hovy, E., Mckeown, K.: Introduction to the Special Issue on Summarization. Computational Linguistics 28(4), 399–408 (2002)

    Article  Google Scholar 

  7. Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: A Theory of Text Organization. Technical Report ISI/RS-87-190 (1987)

    Google Scholar 

  8. Ficher, G., Stevens, C.: Information Access in Complex, Poorly Structure Information Spaces. In: CHI 1991: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 63–70. ACM (1991)

    Google Scholar 

  9. Mihalcea, R.: Graph-Based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization. In: Proceedings of the ACL 2004 (2004)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Kumaresh, N., Ramakrishnan, B.S. (2012). Graph Based Single Document Summarization. In: Kannan, R., Andres, F. (eds) Data Engineering and Management. ICDEM 2010. Lecture Notes in Computer Science, vol 6411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27872-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-27872-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27871-6

  • Online ISBN: 978-3-642-27872-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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