A Systematic Survey of Text Summarization: From Statistical Methods to
  Large Language Models
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.
6675fa008794bbd33f088a8d
Haopeng Zhang
extracted_pending
2024-06-21T22:09:05.326Z
66752f109329dbc7ea458c38
/avatars/b85b08c662407c13461a64d3e632c698.svg
Haopeng Zhang
user
hpzhang94
6675fa008794bbd33f088a8e
Philip S. Yu
6675fa008794bbd33f088a8f
Jiawei Zhang
A Systematic Survey of Text Summarization: From Statistical Methods to
  Large Language Models
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.
A survey examines the evolution of text summarization methods, spanning traditional techniques, deep learning, pre-trained language models, and the recent advancements with large language models.
pre-trained language models large language models deep learning text summarization traditional statistical methods deep learning approaches PLM fine-tuning techniques