A Systematic Review on Text Summarization: Techniques, Challenges, Opportunities

ABSTRACT Text Summarization (TS) is a technique for condensing lengthy text passages. The objective of text summarization is to make concise and coherent summaries that contain the main ideas from a document. When thinking about a page or watching a video, researchers or readers might imagine an abbreviated version which will just catch important parts only. This paper provides an overview of research work done by different authors on this field. There are numerous machine learning and deep learning‐based approaches and methods for implementing text summarization in practice because of several factors like time saving, increased productivity, effective comparative analysis, among others. In this article we explore the concept of text summarization as well as techniques, general framework, applications, evaluation measures within both Indic and Non‐Indic scripts. Additionally, the article brings out some related issues between text summarization and other intelligent systems such as script nature datasets architectures latest works, and so forth. Finally, the authors presented the challenges of text summarization, as well as analytical ideas, conclusions, and future directions for text summarization.

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