SCRS Conference Proceedings on Intelligent Systems
Automatic Summarization of Malayalam Documents using Text Extraction Methods
Authors: Jisha P Jayan and Govindaru Govindaru
Publishing Date: 26-04-2022
ISBN: 978-93-91842-08-6
Abstract
Text summarization is a technique for reducing lengthy passages of text into smaller portions. The goal is to develop a logical and fluent summary that only includes the document's major ideas. It's a important task in Natural Language Processing (NLP) that undoubtedly has a significant impact on synthesization of lengthy documents. With the rise of the digital documentation and publication, devoting time to thoughtfully read an article, document, or book in order to determine its relevance is no longer an option, especially given time constraints. In machine learning and NLP, automatic text summarization is a generic problem. A comparison of text summarization applying the Term Frequency \textemdash Inverse Document Frequency, Latent Semantic Analysis and TextRank algorithms for the Malayalam language is presented in this research paper.
Keywords
Abstractive Summarization, Extractive Summarization, Term Frequency, Inverse Document Frequency, Latent Semantic Analysis, Text Rank.
Cite as
Jisha P Jayan and Govindaru Govindaru, "Automatic Summarization of Malayalam Documents using Text Extraction Methods", In: Raju Pal and Praveen Kumar Shukla (eds), SCRS Conference Proceedings on Intelligent Systems, SCRS, India, 2022, pp. 443-457. https://doi.org/10.52458/978-93-91842-08-6-42