Latent semantic analysis in automatic text summarisation: a state-of-the-art analysis
by Guha Tapas; N. Mehala
International Journal of Intelligence and Sustainable Computing (IJISC), Vol. 1, No. 2, 2021

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.

Online publication date: Fri, 26-Feb-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligence and Sustainable Computing (IJISC):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com