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International Conference on Data Engineering and Management

ICDEM 2010: Data Engineering and Management pp 32–35Cite as

Graph Based Single Document Summarization

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Part of the Lecture Notes in Computer Science book series (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.

Keywords

  • Sentence Scoring Technique
  • Extractive Summarization
  • Single Document Summarization
  • Graph Based Approach
  • Statistical Sentence Extraction

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Author information

Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Trichirappalli, Tamil Nadu, India

    Nandhini Kumaresh & Balasundaram Sadhu Ramakrishnan

Authors
  1. Nandhini Kumaresh
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  2. Balasundaram Sadhu Ramakrishnan
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Bishop Heber College(Autonomous), 620017, Tiruchirappalli, India

    Rajkumar Kannan

  2. National Institute of Informatics (NII), 101-8430, Tokyo, Japan

    Frederic Andres

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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)