A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models

• URL: http://arxiv.org/abs/2406.11289v1
• Date: Mon, 17 Jun 2024 07:52:32 GMT
• Title: A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models
• Authors: Haopeng Zhang, Philip S. Yu, Jiawei Zhang,
• Abstract summary: Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs) This survey provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts.
• Score: 43.37740735934396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed overview of datasets, evaluation metrics, and summarization methods before the LLM era, encompassing traditional statistical methods, deep learning approaches, and PLM fine-tuning techniques, and (2) the first detailed examination of recent advancements in benchmarking, modeling, and evaluating summarization in the LLM era. By synthesizing existing literature and presenting a cohesive overview, this survey also discusses research trends, open challenges, and proposes promising research directions in summarization, aiming to guide researchers through the evolving landscape of summarization research.

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