A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods
It addresses the need for practical, real-world implementations of ATS for researchers and practitioners, though it is incremental as it updates existing survey work.
This survey tackles the problem of outdated and impractical categorizations in automatic text summarization (ATS) by providing a process-oriented overview and reviewing the latest LLM-based methods, resulting in an up-to-date resource that bridges a two-year gap in the literature.
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.