A New LSA and Entropy-Based Approach for Automatic Text Document Summarization

Chandra Yadav and Aditi Sharan
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Aditi Sharan: JNU SC & SS, Delhi, India

International Journal on Semantic Web and Information Systems (IJSWIS), 2018, vol. 14, issue 4, 1-32

Abstract: Automatic text document summarization is active research area in text mining field. In this article, the authors are proposing two new approaches (three models) for sentence selection, and a new entropy-based summary evaluation criteria. The first approach is based on the algebraic model, Singular Value Decomposition (SVD), i.e. Latent Semantic Analysis (LSA) and model is termed as proposed_model-1, and Second Approach is based on entropy that is further divided into proposed_model-2 and proposed_model-3. In first proposed model, the authors are using right singular matrix, and second & third proposed models are based on Shannon entropy. The advantage of these models is that these are not a Length dominating model, giving better results, and low redundancy. Along with these three new models, an entropy-based summary evaluation criteria is proposed and tested. They are also showing that their entropy based proposed models statistically closer to DUC-2002's standard/gold summary. In this article, the authors are using a dataset taken from Document Understanding Conference-2002.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jswis0:v:14:y:2018:i:4:p:1-32

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