title: A Systematic Survey of Text Summarization: From Statistical Methods to
    Large Language Models
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 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.
paper_authors._id: 6675fa008794bbd33f088a8d
paper_authors.name: Haopeng Zhang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-06-21T22:09:05.326Z
paper_authors._id: 66752f109329dbc7ea458c38
paper_authors.avatarUrl: /avatars/b85b08c662407c13461a64d3e632c698.svg
paper_authors.fullname: Haopeng Zhang
paper_authors.type: user
paper_authors.user: hpzhang94
paper_authors._id: 6675fa008794bbd33f088a8e
paper_authors.name: Philip S. Yu
paper_authors._id: 6675fa008794bbd33f088a8f
paper_authors.name: Jiawei Zhang
paper_title: A Systematic Survey of Text Summarization: From Statistical Methods to
    Large Language Models
paper_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 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.
paper_ai_summary: 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.
paper_ai_keywords: pre-trained language models large language models deep learning text summarization traditional statistical methods deep learning approaches PLM fine-tuning techniques