# A PubMed-Scale Dataset of Structured Biomedical Abstracts

arXiv: https://arxiv.org/abs/2606.11361 · HTML: https://mlsanity.com/paper/2606.11361/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Accelerating Clinical Evidence Synthesis with Large Language Models](https://mlsanity.com/paper/2406.17755/) (2024)
- [Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task](https://mlsanity.com/paper/2201.07112/) (2022)
- [MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts](https://mlsanity.com/paper/2604.06505/) (2026)

## Abstract (arXiv)

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.