---
license: cc-by-4.0
task_categories:
- summarization
language:
- en
size_categories:
- n<1K
---
---
license: apache-2.0
task_categories:
- summarization
language:
- en
tags:
- biomedical
- health
- NLP
- summarization
- LLM
size_categories:
- 1K<n<10K
---

PlainFact-summary is a high-quality human-annotated dataset designed for Plain Language Summarization tasks, along with [PlainQAFact](https://github.com/zhiwenyou103/PlainQAFact) factuality evaluation framework. It is collected from the [Cochrane database](https://www.cochranelibrary.com/) sampled from CELLS dataset ([Guo et al., 2024](https://doi.org/10.1016/j.jbi.2023.104580)). 

We also provided a sentence-level version [PlainFact](https://huggingface.co/datasets/uzw/PlainFact) that split the summaries into sentences with fine-grained explanation annotations. In total, we have 200 plain language summary-abstract pairs.

Here are explanations for the headings:
- **Target_Sentence**: The plain language sentence/summary.
- **Original_Abstract**: The scientific abstract corresponding to each sentence/summary.

You can load our dataset as follows:
```python
from datasets import load_dataset
plainfact = load_dataset("uzw/PlainFact-summary")
```

For detailed information regarding the dataset or factuality evaluation framework, please refer to our [Github repo](https://github.com/zhiwenyou103/PlainQAFact) and paper.

Citation
If you use data from PlainFact or PlainFact-summary, please cite with the following BibTex entry:
```
@misc{you2025plainqafactautomaticfactualityevaluation,
      title={PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation}, 
      author={Zhiwen You and Yue Guo},
      year={2025},
      eprint={2503.08890},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.08890}, 
}
```