---
license: cc-by-sa-3.0
task_categories:
- summarization
language:
- en
tags:
- biomedical
- health
- NLP
- summarization
- LLM
- factuality
size_categories:
- 1K<n<10K
---

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)). 

We also provided a summary-level version [PlainFact-summary](https://huggingface.co/datasets/uzw/PlainFact-summary) that aggregrated all the sentences as complete summaries. In total, we have 200 plain language summary-abstract pairs.

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!

Here are explanations for the headings:
- **Target_Sentence**: The plain language sentence/summary.
- **Original_Abstract**: The scientific abstract corresponding to each sentence/summary.
- **External**: Whether the sentence includes information does not explicitly present in the scientific abstract. (yes: explanation, no: simplification)

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

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:
```
```