license: cc-by-4.0
task_categories:
- summarization
language:
- en
size_categories:
- n<1K
license: apache-2.0 task_categories: - summarization language: - en tags: - biomedical - health - NLP - summarization - LLM size_categories: - 1K<n<10K
PlainFact-summary is a high-quality human-annotated dataset designed for Plain Language Summarization tasks, along with PlainQAFact factuality evaluation framework. It is collected from the Cochrane database sampled from CELLS dataset (Guo et al., 2024).
We also provided a sentence-level version 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:
- Target_Sentence: The plain language sentence/summary.
- Original_Abstract: The scientific abstract corresponding to each sentence/summary.
You can load our dataset as follows:
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 and paper.
Citation If you use data from PlainFact or PlainFact-summary, please cite with the following BibTex entry:
@misc{you2025plainqafactautomaticfactualityevaluation,
title={PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation},
author={Zhiwen You and Yue Guo},
year={2025},
eprint={2503.08890},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.08890},
}