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 factuality evaluation framework. It is collected from the Cochrane database sampled from CELLS dataset (Guo et al., 2024).
We also provided a summary-level version 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:
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 and paper.
Citation If you use data from PlainFact or PlainFact-summary, please cite with the following BibTex entry: