| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - medical |
| - biomedical |
| - abstract |
| - conclusion-generation |
| pretty_name: MedConclusion Compact |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # MedConclusion-Compact |
|
|
| **MedConclusion** is a large-scale dataset of 5.7M PubMed structured abstracts for biomedical conclusion generation. Each instance pairs the non-conclusion sections of an abstract with the original author-written conclusion, providing naturally occurring supervision for evidence-to-conclusion reasoning. MedConclusion also includes journal-level metadata such as biomedical category and SJR, enabling subgroup analysis across biomedical domains. |
|
|
| This repository contains the **Compact** version of the dataset, designed for faster evaluation and model prototyping. For the full dataset (5.7M instances), please check out the [**Full Version**](https://huggingface.co/datasets/harvardairobotics/MedConclusion). |
|
|
| - **Train**: 100,000 instances |
| - **Validation**: 10,000 instances |
| - **Test**: 30,000 instances |
|
|
| ## Benchmark Information |
|
|
| This dataset is introduced in the paper [MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts](https://arxiv.org/abs/2604.06505). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{li2026medconclusion, |
| title={MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts}, |
| author={Li, Weiyue and Qian, Ruizhi and Li, Yi and Li, Yongce and Long, Yunfan and Cai, Jiahui and Luo, Yan and Wang, Mengyu}, |
| journal={arXiv preprint arXiv:2604.06505}, |
| year={2026} |
| } |
| ``` |
|
|
