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Summarization of text-based documents with a determination of latent topical sections and information-rich sentences

Summarization of text-based documents with a determination of latent topical sections and... A method is proposed for use in summarization of text-based documents. By means of the method it is possible to discover latent topical sections and information-rich sentences. The underlying basis of the method — clustering of sentences — is formulated mathematically in the form of a problem of quadratic-type integer programming. An algorithm that makes it possible to determine with specified precision the optimal number of clusters is developed. The synthesis of a neural network is described for the purpose of solving a problem of integer quadratic programming. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

Summarization of text-based documents with a determination of latent topical sections and information-rich sentences

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Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2007
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411607030030
Publisher site
See Article on Publisher Site

Abstract

A method is proposed for use in summarization of text-based documents. By means of the method it is possible to discover latent topical sections and information-rich sentences. The underlying basis of the method — clustering of sentences — is formulated mathematically in the form of a problem of quadratic-type integer programming. An algorithm that makes it possible to determine with specified precision the optimal number of clusters is developed. The synthesis of a neural network is described for the purpose of solving a problem of integer quadratic programming.

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Jun 1, 2007

Keywords: summarization; clustering; optimal number of clusters; information-rich sentence; neural networks

References

  • Automated Text Structuring and Summarization
    Salton, G.; Singhal, A.; Mitra, M.; Buckley, C.
  • Text Summarization Using a Trainable Summarizer and Latent Semantic Analysis
    Yeh, J.-Y.; Ke, H.-R.; Yang, W.-P.; Meng, I.-H.
  • The Automatic Creation of Literature Abstracts
    Luhn, H.P.
  • Techniques of Cluster Algorithms in Data Mining
    Grabmeier, J.; Rudolph, A.
  • Mathematical Programming for Data Mining: Formulations and Challenges
    Bradley, P.S.; Fayyad, U.M.; Mangasarian, O.L.
  • New Algorithms for Multi-class Diagnosis Using Tumor Gene Expression Signature
    Bagirov, A.M.; Ferguson, B.; Ivkovic, S.; Saunders, G.; Yearwood, J.
  • On Cluster Validity Index for Estimation of the Optimal Number of Fuzzy Clusters
    Kim, D.-W.; Lee, K.H.; Lee, D.