Advancements in Automatic Text Summarization using Natural Language Processing

Roha Irfan; Rabia tehseen; Anam Mustaqeem; Ramsha Saeed; Usman Aamer; Jawad Hassan

Authors

Roha Irfan Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Rabia tehseen Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Anam Mustaqeem Department of Software Engineering, University of Central Punjab, Lahore, Pakistan
Ramsha Saeed Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Usman Aamer Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Jawad Hassan Department of Computer Science, University of Central Punjab, Lahore, Pakistan

Keywords:

Natural language processing (NLP), Text summarization, Automatic text Summarization, Extractive Method (EXT), Abstractive Method (ABS), Deep learning

Abstract

With the rapid expansion of data across various domains, the need for automated text summarization has become increasingly crucial. Given the overwhelming volume of textual and numerical data, effective summarization techniques are required to extract key information while preserving content integrity. Text summarization has been a subject of research for decades, with various approaches developed using natural language processing (NLP) and a combination of different algorithms. This paper is an SLR-type essay presenting the existing text summarization techniques and their evaluation. It covers the basic concepts behind extractive and abstractive summarization and how deep learning models could serve as a boost in the performance of summarization. The study goes on to investigate the present use of text summarization in different areas and looks into the various methodologies applied in this area. A total of twenty-four carefully selected research articles were being analyzed to identify key trends, challenges, and limitations regarding text summarization techniques. The paper further discusses the existing literature and proposes a number of open research challenges with insight concerning possible future directions in text summarization.

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