A Review- Machine Learning Techniques for Text Summarization

DOI

https://www.doi.org/10.59256/indjcst.20240301011

Published In

Volume 3, Issue 1 (2024)

Published Online

January-April 2024

Pages

74-77

Article Type

Research Article

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Aspect ranking framework is significant to identify the important aspects from numerous consumer reviews posted in various domains like hotel, movie and product etc. They could be broadly classified into supervised and unsupervised approaches broadly. Supervised methods rely on semantic knowledge bases. These are found to be effective for ranking compared to conventional approaches. These methods available in the literature are discussed in detail. Next, this review focuses on the extractive summarization systems, in which the summary is generated by picking a sub-set of sentences from the related text. Extractive summarization systems that utilize machine learning, optimization and map reduce framework are explained elaborately. This is due to the efficiency of these techniques reported in the comprehensive works available for text summarization. A literature covering text similarity discovery methods employing text, semantic information and graph based systems are presented in detail at the end of the chapter. Among these graph based methods play a vital role in current field of the research