Title: Latent semantic analysis in automatic text summarisation: a state-of-the-art analysis

Authors: Guha Tapas; N. Mehala

Addresses: Department of CSE, Presidency University, Bengaluru, India ' Department of CSE, Presidency University, Bengaluru, India

Abstract: Increasing availability of information in the web and its ease of access necessitate the need for efficient and effective automatic text summarisation. Automatic text summarisation condenses the source text (a single document or multiple documents) into a compact version preserving its overall meaning and information content. Till now, researchers have employed different approaches for creating well-formed summaries. One of the most popular methods is the latent semantic analysis (LSA). In this paper, various prominent works to produce extractive and abstractive text summaries based on different variants of LSA algorithm are analysed.

Keywords: information retrieval; automatic text summarisation; latent semantic analysis; LSA; singular value decomposition; SVD.

DOI: 10.1504/IJISC.2021.113294

International Journal of Intelligence and Sustainable Computing, 2021 Vol.1 No.2, pp.128 - 137

Received: 07 May 2019
Accepted: 18 Aug 2019
Published online: 26 Feb 2021 *

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