A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models
<|reference_start|>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.<|reference_end|>
@article{zhang2024a,
    title={A Systematic Survey of Text Summarization: From Statistical Methods to
  Large Language Models},
    author={Haopeng Zhang, Philip S. Yu, Jiawei Zhang},
    journal={arXiv preprint arXiv:2406.11289},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11289},
    primaryClass={cs.CL}
}