Advancements in Text Summarization Through Machine Learning: A Comprehensive Survey and Analysis

Abstract

Due to the extensive amount of text available for any given task, for instance, a research project, it has become a need to have the gist of these documents in a succinct format. In this review paper, we discussed various methods used for single and multi-document summarization. It explores extractive, abstractive, and hybrid methods, along with the role of deep learning models like RNNs, CNNs, and transformers. The survey examines datasets, evaluation metrics, recent advancements, and future scopes in this field. A comparative analysis of methodologies and approaches is also presented.