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--- |
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language: |
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- en |
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license: cc-by-sa-3.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- summarization |
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tags: |
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- biomedical |
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- health |
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- NLP |
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- summarization |
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- LLM |
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- factuality |
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--- |
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PlainFact is a high-quality human-annotated dataset with fine-grained explanation (i.e., added information) annotations 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)). |
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PlainFact is a sentence-level benchmark that splits the summaries into sentences with fine-grained explanation annotations. In total, we have 200 plain language summary-abstract pairs (2,740 sentences). |
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In addition to all factual plain language sentences, we also generate contrasting non-factual examples for each plain language sentence. 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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> Currently, we only released the annotation for **Explanation** sentences. We will release the full version of PlainFact soon (including Category and Relation information). Stay tuned! |
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Here are explanations for the headings: |
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- **Target_Sentence_factual**: The all factual plain language sentence. |
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- **Target_Sentence_non_factual**: The perturbed (non-factual) plain language sentence. |
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- **External**: Whether the sentence includes information does not explicitly present in the scientific abstract. (yes: explanation, no: simplification) |
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- **Original_Abstract**: The scientific abstract corresponding to each sentence/summary. |
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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") |
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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 at https://huggingface.co/papers/2503.08890. |
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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: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation}, |
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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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``` |
