A Systematic Survey of Text Summarization: From Statistical Methods to
  Large Language Models
Authors: name: Haopeng Zhang, name: Philip S. Yu, name: Jiawei Zhang

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.