Structured abstracts are important for biomedical literature processing, by facilitating information retrieval, text mining, and knowledge synthesis. However, a vast portion of abstracts indexed in PubMed remain unstructured, presenting a significant bottleneck for downstream text-processing workflows and applications. To resolve this limitation, we introduce Structured PubMed, a comprehensive corpus of section-labeled biomedical abstracts compiled from the complete PubMed database, encompassing over 23.2 million research-article records. The corpus is divided into two distinct subsets: a collection of 5.9 million author-structured abstracts parsed from official XML files, and an automatically labeled collection of 17.2 million originally unstructured abstracts structured via a verbatim-extraction Large Language Model pipeline. Every record is harmonized under a unified five-section schema and mapped to its original PubMed identifier, publication type, and publication date. This dataset can be utilized to train sentence-classification models, benchmark text-segmentation architectures, and perform large-scale, section-specific information extraction at an unprecedented PubMed-wide scale.

翻译：结构化摘要对生物医学文献处理至关重要，能够促进信息检索、文本挖掘和知识综合。然而，PubMed索引中的大部分摘要仍为非结构化格式，这给下游文本处理工作流程和应用造成了显著瓶颈。为解决这一局限，我们推出了Structured PubMed——一个基于完整PubMed数据库构建的、涵盖超过2320万条研究论文记录的章节标注生物医学摘要综合语料库。该语料库分为两个独立子集：一个包含590万条作者结构化摘要（从官方XML文件中解析得出），另一个包含1720万条原本非结构化摘要（通过逐字提取大语言模型流程自动标注生成）。每条记录均统一采用五部分章节模式进行规范化，并映射至其原始PubMed标识符、发表类型及发表日期。该数据集可用于训练句子分类模型、评估文本分割架构、以及在PubMed全库规模下开展前所未有的章节特异性信息提取工作。