A Novel Summarization-based Approach for Feature Reduction Enhancing Text Classification Accuracy

Authors

  • S. Rahamat Basha Department of Computer Science & Technology, Sri Krishnadevaraya University, India http://orcid.org/0000-0003-3262-6350
  • J. Keziya Rani Department of Computer Science & Technology, Sri Krishnadevaraya University, India
  • J. J. C. Prasad Yadav Department of CSE, Rajeev Gandhi Memorial College of Engineering and Technology, India
Volume: 9 | Issue: 6 | Pages: 5001-5005 | December 2019 | https://doi.org/10.48084/etasr.3173

Abstract

Automatic summarization is the process of shortening one (in single document summarization) or multiple documents (in multi-document summarization). In this paper, a new feature selection method for the nearest neighbor classifier by summarizing the original training documents based on sentence importance measure is proposed. Our approach for single document summarization uses two measures for sentence similarity: the frequency of the terms in one sentence and the similarity of that sentence to other sentences. All sentences were ranked accordingly and the sentences with top ranks (with a threshold constraint) were selected for summarization. The summary of every document in the corpus is taken into a new document used for the summarization evaluation process.

Keywords:

summarization, dimension reduction, feature selection, feature extraction, feature clustering, text classification

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References

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How to Cite

[1]
Rahamat Basha, S., Keziya Rani, J. and Prasad Yadav, J.J.C. 2019. A Novel Summarization-based Approach for Feature Reduction Enhancing Text Classification Accuracy. Engineering, Technology & Applied Science Research. 9, 6 (Dec. 2019), 5001–5005. DOI:https://doi.org/10.48084/etasr.3173.

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