A survey of text summarization: Techniques, evaluation and challenges

Abstract

This paper explores the complex field of text summarization in Natural Language Processing (NLP), with particular attention to the development and importance of semantic understanding. Text summarization is a crucial component of natural language processing (NLP), which helps to translate large amounts of textual data into clear and understandable representations. As the story progresses, it demonstrates the dynamic transition from simple syntactic structures to sophisticated models with semantic comprehension. In order to effectively summarize, syntactic, semantic, and pragmatic concerns become crucial, highlighting the necessity of capturing not only grammar but also the context and underlying meaning. It examines the wide range of summarization models, from conventional extractive techniques to state-of-the-art tools like pre-trained models. Applications are found in many different fields, demonstrating how versatile summarizing techniques are. Semantic drift and domain-specific knowledge remain obstacles, despite progress. In the future, the study predicts developments like artificial intelligence integration and transfer learning, which motivates academics to investigate these prospects for advancement. The approach, which is based on the PRISMA framework, emphasizes a methodical and open literature review. The work attempts to further natural language processing (NLP) and text summarization by combining various research findings and suggesting future research directions in this dynamic subject.