An Extensive Survey on Investigation Methodologies for Text Summarization

Abstract

Natural language processing (NLP) is a fast-expanding field, and text summarization has recently gained a lot of research interest. The necessity for automatic summarizing approaches to effectively digest massive amounts of textual data has grown in importance, due to the plethora (excessive amount of something) of information available in the digital age [18]. By automatically producing succinct and educational summaries of extensive materials, NLP-based text summarizing systems have the potential to revolutionize the way humans consume and process information. This review paper offers a thorough examination of the text summarizing research approaches. The process of creating a concise and useful summary of a text document is called text summarization. Even for cutting-edge natural language processing (NLP) systems, it is a difficult task. It was carried out using a thorough analysis of the most recent text summarizing research. The evaluation revealed a variety of research approaches that have been employed in the creation and assessment of text summarizing systems. This study's key discovery is that there are numerous different investigative approaches that can be used for text summarizing. These methods can be roughly divided into two groups: • Extractive text summarization • Abstractive text summarization During the review we found that extractive summarization is a fairly simple method as it selects the key phrases from a text and extracts them to create a summary while abstractive summarization presents data in a clearer, more informative fashion by producing a summary. This review was important because it gives a thorough overview of the research approaches utilized for text summarizing, this article is significant. Researchers and programmers can utilize this data to create brand-new, improved text summarizing systems. [20]