Advancements in Automatic Text Summarization using Natural Language Processing

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 investigatesthe 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. Itproposes a number of open research challenges with insight concerning possible future directions in text summarization.