Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP) algorithms, aims to create concise and accurate summaries, thereby significantly reducing the human effort required in processing large volumes of text. ATS has drawn considerable interest in both academic and industrial circles. Many studies have been conducted in the past to survey ATS methods; however, they generally lack practicality for real-world implementations, as they often categorize previous methods from a theoretical standpoint. Moreover, the advent of Large Language Models (LLMs) has altered conventional ATS methods. In this survey, we aim to 1) provide a comprehensive overview of ATS from a ``Process-Oriented Schema'' perspective, which is best aligned with real-world implementations; 2) comprehensively review the latest LLM-based ATS works; and 3) deliver an up-to-date survey of ATS, bridging the two-year gap in the literature. To the best of our knowledge, this is the first survey to specifically investigate LLM-based ATS methods.

翻译：自动文本摘要技术利用自然语言处理算法，旨在生成简洁准确的摘要，从而大幅减少处理大量文本所需的人力投入。该技术在学术界和工业界都引起了广泛关注。过去已有诸多研究对自动文本摘要方法进行综述，但这些综述通常缺乏对实际部署的实用性，因为它们倾向于从理论视角对既有方法进行分类。此外，大语言模型的出现改变了传统的自动文本摘要方法。本综述旨在：1）从最贴合实际应用的“面向过程范式”角度，提供自动文本摘要的全面概述；2）系统梳理最新的基于大语言模型的自动文本摘要研究成果；3）呈现一份最新的自动文本摘要综述，填补该领域近两年的文献空白。据我们所知，这是首篇专门探讨基于大语言模型的自动文本摘要方法的综述。