A survey on biomedical automatic text summarization with large language models
Zhenyu Huang, Xianlai Chen, Yunbo Wang, Jincai Huang, Xing Zhao
Abstract
Automatic text summarization in the biomedical field can support efficient literature screening, medical knowledge management, and innovative medical research. In recent years, Large Language Models (LLMs), as a disruptive technology in natural language processing, have shown great potential for Biomedical Automatic Text Summarization (BATS). This technology helps to better understand the terminology of biomedical texts, track medical hotspots, and generate personalized diagnoses and treatment plans. This paper provides an in-depth discussion on the development of BATS, and the opportunities as well as challenges brought by applying LLMs to biomedical automatic text summarization. Firstly, the development of BATS is reviewed, where traditional text summarization, neural network-based summarization, and LLMs-based summarization are analyzed systematically. Meanwhile, the applications of various LLMs (e.g., BERT and GPT series) in three types of BATS are presented in detail, including extractive summarization, abstractive summarization, and hybrid summarization. Next, the relevant datasets are introduced, such as PubMed, COVID-19 and MIMIC-Ⅲ. Then, traditional, emerging, and auxiliary metrics for evaluating the performance of BATS are shown, and the performance evaluation of different models is elaborated. Finally, the opportunities brought by applying LLMs to BATS are described, and the potential challenges along with the corresponding solutions are discussed.