add non-factual instances
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README.md
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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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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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- **External**: Whether the sentence includes information does not explicitly present in the scientific abstract. (yes: explanation, no: simplification)
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You can load our dataset as follows:
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```python
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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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