Review on Text Summarization using Clustering and Machine Learning-Deep Learning Models

Most of the data created by humans comes from blogs, digital magazines, social networking sites, and news websites. Extracting the essential information from a text through hand summarization is time-consuming and overwhelming. In general, a text summary is a technique for producing a condensed or shortened version of a text document that includes pertinent information for readers. Summarization approaches use computational techniques to produce a reduced version of a text while retaining its original meaning. As social networking sites, eBooks, and e-papers continue to expand, transliterated terms are becoming more and more common in text corpora. This research study intends to conduct a review of text summarization by reviewing 20 papers. The review includes the following contribution. (i) Reviews the methods (machine learning/deep learning/clustering techniques) used for text summarization in the selected papers. (ii) Reviews the features considered for the summarization process. (iii) Reviews the languages considered. (iv) Reviews the performance metrics used and the maximum performance obtained by the methods. (v) Determines the research gaps and challenges.