Various Approaches Of Text Summarization: A Literature Review
DOI:
https://doi.org/10.29284/d30znw81Keywords:
Summarization, Automatic, Deep Learning, Evaluation, Extraction, Abstraction.Abstract
In the digital age, a vast reservoir of unstructured data has become readily accessible, offering opportunities for intelligent applications such as data analysis, Automatic Text Summarization (ATS), caption or headline generation, and rumor detection, among many others. There are different approaches in text mining; summarization is one of those approaches. Text summarization automatically creates a brief overview of a lengthy text document while retaining crucial details. Text summarization has many techniques or approaches according to need and expectation. In this review paper we describe the research scenario of text summarization from 1958 to 2024. The purpose of this paper is to understand the evolution between old techniques and new techniques. It presents a variety of datasets, assessment strategies, and automatic text summary approaches. Nowadays, deep learning provides significant results in text summarization. Selecting key phrases from the source text that accurately communicate the original meaning is crucial when summarizing text. These surveys do provide a comprehensive evaluation framework for assessing the approaches mentioned. Therefore, this survey provides three main contributions: 1) Explanation of techniques and classification of text summarization methods based on expected output and available data. 2) An exploration of datasets along with their evaluation techniques and results as discussed in some papers. 3) Identification of research gaps for further study. This paper provides an overview of text summarization and identifies opportunities for further research.
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