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--- |
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language: |
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- en |
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license: cc-by-4.0 |
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size_categories: |
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- n<1K |
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task_categories: |
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- summarization |
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--- |
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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)). |
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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/)). |
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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. |
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Here are explanations for the headings: |
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- **Factual**: "yes": the plain language summary is factual; "no": the plain language summary is non-factual after applying faithfulness perturbation. |
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- **Target_Sentence**: The plain language summary. |
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- **Original_Abstract**: The scientific abstract corresponding to each sentence/summary. |
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> Note: the number of factual and non-factual plain language summaries is the same (200 for each). |
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You can load our dataset as follows: |
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```python |
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from datasets import load_dataset |
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plainfact = load_dataset("uzw/PlainFact-summary") |
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``` |
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For detailed information regarding the dataset or factuality evaluation framework, please refer to our [Github repo](https://github.com/zhiwenyou103/PlainQAFact) and paper. |
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Citation |
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If you use data from PlainFact or PlainFact-summary, please cite with the following BibTex entry: |
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``` |
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@misc{you2025plainqafactautomaticfactualityevaluation, |
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title={PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization}, |
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author={Zhiwen You and Yue Guo}, |
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year={2025}, |
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eprint={2503.08890}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2503.08890}, |
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} |
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``` |
