The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), poses a significant challenge for clinicians and researchers to access clinical information efficiently. To tackle this challenge, biomedical text summarization (BTS) has been proposed as a solution to support clinical information retrieval and management. BTS aims at generating concise summaries that distill key information from single or multiple biomedical documents. In recent years, the rapid advancement of fundamental natural language processing (NLP) techniques, from pre-trained language models (PLMs) to large language models (LLMs), has greatly facilitated the progress of BTS. This growth has led to numerous proposed summarization methods, datasets, and evaluation metrics, raising the need for a comprehensive and up-to-date survey for BTS. In this paper, we present a systematic review of recent advancements in BTS, leveraging cutting-edge NLP techniques from PLMs to LLMs, to help understand the latest progress, challenges, and future directions. We begin by introducing the foundational concepts of BTS, PLMs and LLMs, followed by an in-depth review of available datasets, recent approaches, and evaluation metrics in BTS. We finally discuss existing challenges and promising future directions in the era of LLMs. To facilitate the research community, we line up open resources including available datasets, recent approaches, codes, evaluation metrics, and the leaderboard in a public project: https://github.com/KenZLuo/Biomedical-Text-Summarization-Survey/tree/master. We believe that this survey will be a useful resource to researchers, allowing them to quickly track recent advancements and provide guidelines for future BTS research within the research community.

翻译：生物医学文献和电子健康记录等生物医学文本的指数级增长，给临床医生和研究人员高效获取临床信息带来了重大挑战。为应对这一挑战，生物医学文本摘要（BTS）被提出作为支持临床信息检索和管理的解决方案。BTS旨在从单个或多个生物医学文档中提炼关键信息，生成简洁摘要。近年来，从预训练语言模型（PLMs）到大语言模型（LLMs）的基础自然语言处理技术的快速发展，极大地推动了BTS的进展。这一发展带来了大量摘要方法、数据集和评估指标的提出，凸显了对BTS进行全面且最新综述的需求。本文系统回顾了BTS的最新进展，利用从PLMs到LLMs的尖端NLP技术，以帮助理解最新进展、挑战和未来方向。我们首先介绍BTS、PLMs和LLMs的基础概念，随后深入回顾BTS中可用的数据集、最新方法和评估指标。最后，我们讨论了LLM时代存在的挑战和有前景的未来方向。为便于研究界使用，我们在公共项目中整理了开放资源，包括可用数据集、最新方法、代码、评估指标和排行榜：https://github.com/KenZLuo/Biomedical-Text-Summarization-Survey/tree/master。我们相信本综述将成为研究人员的有用资源，使他们能够快速跟踪最新进展，并为未来研究社区内的BTS研究提供指南。