---
language:
- en
license: cc-by-4.0
size_categories:
- n<1K
task_categories:
- summarization
---

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, as described in [PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation](https://huggingface.co/papers/2503.08890). 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)). 
In addition to using all factual plain language summaries, we also generate contrasting non-factual examples for each plain language summary. These contrasting examples are perturbed using GPT-4o, following the perturbation criteria for faithfulness introduced in APPLS ([Guo et al., 2024](https://aclanthology.org/2024.emnlp-main.519/)).

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:
- **Factual**: "yes": the plain language summary is factual; "no": the plain language summary is non-factual after applying faithfulness perturbation.
- **Target_Sentence**: The plain language summary.
- **Original_Abstract**: The scientific abstract corresponding to each sentence/summary.

> Note: the number of factual and non-factual plain language summaries is the same (200 for each).

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: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization}, 
      author={Zhiwen You and Yue Guo},
      year={2025},
      eprint={2503.08890},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.08890}, 
}
```