Lexical Cohesion Based Topic Modeling for Summarization

  • Gonenc Ercan
  • Ilyas Cicekli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4919)

Abstract

In this paper, we attack the problem of forming extracts for text summarization. Forming extracts involves selecting the most representative and significant sentences from the text. Our method takes advantage of the lexical cohesion structure in the text in order to evaluate significance of sentences. Lexical chains have been used in summarization research to analyze the lexical cohesion structure and represent topics in a text. Our algorithm represents topics by sets of co-located lexical chains to take advantage of more lexical cohesion clues. Our algorithm segments the text with respect to each topic and finds the most important topic segments. Our summarization algorithm has achieved better results, compared to some other lexical chain based algorithms.

Keywords

text summarization lexical cohesion lexical chains 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barzilay, R., Elhadad, M.: Using Lexical Chains for Text Summarization. In: Mani, I., Maybury, M.T. (eds.) Advances in Automatic Text Summarization, pp. 111–121. MIT Press, Cambridge (1999)Google Scholar
  2. 2.
    Brunn, M., Chali, Y., Pinchak, C.J.: Text summarization using lexical chains. In: Proceedings of the Document Understanding Conference (DUC 2001), New Orleans, LA (2001)Google Scholar
  3. 3.
    Chali, Y., Kolla, M.: University of lethridge summarizer at DUC04. In: Proceedings of the Document Understanding Conference (DUC 2004), Boston, USA (2004)Google Scholar
  4. 4.
    Doran, W.P., et al.: Assessing the impact of lexical chain scoring methods and sentence extraction schemes on summarization. In: Proceedings of the 5th International Conferences on Intelligent Text Processing and Computational Linguistics (CICLing-2004), pp. 627–635 (2004)Google Scholar
  5. 5.
    Doran, W., et al.: News story gisting at university college dublin. In: Proceedings of the Document Understanding Conference (DUC 2004), Boston, USA (2004)Google Scholar
  6. 6.
    Ercan, G., Cicekli, I.: Using lexical chains for keyword extraction. Information Processing & Management 43, 1705–1714 (2007)CrossRefGoogle Scholar
  7. 7.
    Galley, M., McKeown, K.: Improving word sense disambiguation in lexical chaining. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), pp. 1486–1488 (2003)Google Scholar
  8. 8.
    Halliday, M., Hasan, R.: Cohesion in English. Longman, London (1976)Google Scholar
  9. 9.
    Lin, C.Y., Hovy, E.H.: Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of HLT-NAACL-2003, Edmenton, Canada (2003)Google Scholar
  10. 10.
    Morris, J., Hirst, G.: Lexical cohesion computed by thesaural relations as an indicator of the structure of text. Computational Linguistics 17, 21–43 (1991)Google Scholar
  11. 11.
    Toutanova, K., et al.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of HLT-NAACL-2003, Edmenton, Canada (2003)Google Scholar
  12. 12.
    Proceedings of the Document Understanding Conference (DUC 2004), Boston, USA (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gonenc Ercan
    • 1
  • Ilyas Cicekli
    • 1
  1. 1.Dept. of Computer EngineeringBilkent UniversityAnkaraTurkey

Personalised recommendations