构建了2320万篇结构化医学文献摘要数据集，助力文本挖掘与知识提取。
A PubMed-Scale Dataset of Structured Biomedical Abstracts
- 从PubMed全库提取并结构化2320万篇摘要，分两类：作者标注590万条，模型自动标注1720万条。
- 所有摘要统一为五段式框架，保留原始文献标识、发表类型和日期信息。
- 适合训练分类模型、测试分割算法或开展大规模领域特定信息抽取研究。
结构化摘要对生物医学文献处理至关重要，有助于信息检索、文本挖掘和知识整合。然而，大量索引于PubMed的摘要仍为非结构化状态，成为下游文本处理流程的瓶颈。为此，我们推出了Structured PubMed，一个涵盖超过2320万条研究论文记录的结构化摘要语料库，源自完整的PubMed数据库。该语料库分为两个子集：从官方XML文件解析出的590万条作者结构化摘要，以及通过逐字提取大语言模型管道自动标注的1720万条原非结构化摘要。每条记录均采用统一的五段式结构框架，并映射至其原始PubMed标识符、出版类型和出版日期。该数据集可用于训练句子分类模型、评估文本分割架构，以及在前所未有的全PubMed尺度上实现分章节信息抽取。
原文摘要 · Abstract (English)
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.
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