A comprehensive survey on automatic text summarization with exploration of LLM-based methods

The exponential growth of textual content on the internet, alongside vast archives of news articles, scientific papers, legal documents, and other domains, has made Automatic Text Summarization (ATS) increasingly important. ATS aims to create concise and accurate summaries, significantly reducing the e ff ort required to process large volumes of text. Originating in the 1950s, ATS has evolved through several technical shifts, moving from statistical models to machine learning and deep learning approaches, and more recently, to pre-trained models. Previous surveys have focused on conventional ATS methods, which are often constrained by predefined generative paradigms. However, the advent of Large Language Models (LLMs) has introduced a paradigm-flexible approach to summarization. With their superior generative capabilities, in-context learning, and few-shot learning abilities, LLMs have demonstrated remarkable improvements in coherence, fluency, and overall summarization quality. In this survey, we provide a comprehensive review of both conventional ATS approaches and the latest advancements in LLM-based methods. Additionally, we propose a novel retrieval algorithm designed to e ffi ciently collect relevant papers, which could be adapted for use in other types of surveys. Our contributions are threefold: (1) o ff ering an up-to-date survey of ATS, (2) reviewing the latest LLM-based summarization methods, and (3) introducing a new retrieval algorithm for paper collection.