A SURVEY OF TEXT SUMMARIZATION TECHNIQUES: RECENT ADVANCES AND CHALLENGES

Abstract

Text summarization addresses the fundamental need for efficient information processing and comprehension in an era characterized by information abundance. It enhances productivity, decision-making and accessibility while enabling users to navigate and extract value from the ever-expanding universe of textual data. Text summarization, a crucial facet of natural language processing, has garnered considerable attention for its capacity to extract the essential information from vast textual data. Text summarization aims to condense extensive textual documents into concise representations, facilitating efficient comprehension and information retrieval across diverse domains like document summarization, news aggregation and automatic abstract generation. Techniques encompass extractive and abstractive methods, spanning various granularities from sentence-level to document-level summarization, addressing challenges in content selection, coherence preservation and multi-document summarization. Recent research employs statistical algorithms, graph-based approaches, neural networks and reinforcement learning, leveraging linguistic features and semantic representations for coherent summaries. Advancements in summarization have enhanced quality and coherence, with extractive techniques using graph algorithms and abstractive methods employing neural networks showing promise. Summarization significantly impacts information retrieval, document understanding and knowledge discovery, aiding productivity and decision-making. In conclusion, text summarization plays a pivotal role in managing information overload and extracting actionable insights. Future research may focus on enhancing model robustness and interpretability to meet evolving demands, solidifying text summarization as a foundational tool in natural language processing.