Advertisement

Query-Based Automatic Multi-document Summarization Extraction Method for Web Pages

  • Qi He
  • Hong-Wei Hao
  • Xu-Cheng Yin
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 144)

Abstract

In order to overcome the shortcomings of the incomprehensive of traditional automatic summarization, this paper proposes the automatic multi-document summarization extraction method based on user’s query for web pages. The key technology in our method is the sentence importance weight calculation, which takes varieties of impact factors into account to score the candidate sentence importance weight in the retrieval results. These impact factors include the segmentation results weight, characteristics of sentence structure, length of sentence and the mutual information of search terms. On the basis of our method, this paper gives a description of the automatic summarization process. Then, the comparative experimental results show that our method is more effective on the Precision and Recall than others in abstract extraction.

Keywords

Mutual Information Segmentation Result Latent Semantic Analysis Retrieval Result Declarative Sentence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001) ISBN 1558604898Google Scholar
  2. 2.
    Luhn, H.P.: The automatic creation of literature abstract. IBM Journal of Research and Development 2(2), 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Wang, J., Wu, G., Zhou, Y., Zhang, F.: Research on Automatic Summarization of Web Document Guided by Discourse. Journal of Computer Research and Development, 398–405 (2003)Google Scholar
  4. 4.
    Tadashi, N., Yuji, M.: A New Approach to Unsupervised Text Summarization. In: Proceedings of ACM SIGIR 2001, pp. 26–34 (2001)Google Scholar
  5. 5.
    Conroy, J.M., Schlesinger, J.D.: CLASSY 2007 at DUC 2007. In: Proceedings of the 2007 Document Understanding Conference (DUC 2007), New York (2007)Google Scholar
  6. 6.
    Gong, Y.H., Liu, X.: Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis. In: Processing of ACM SIGIR 2001, pp. 19–25 (2001)Google Scholar
  7. 7.
    Gong, Y., Liu, X.: Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis. In: Proceedings of ACM SIGIR 2001, pp. 19–25 (2001)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.University of Science and Technology BeijingBeijingChina

Personalised recommendations