Improve dataset card: Add task categories, tags, and update paper title

README.md

@@ -6,9 +6,17 @@ size_categories:
 - n<1K
 task_categories:
 - summarization
+- question-answering
+- text-classification
+tags:
+- biomedical
+- medical-domain
+- factual-consistency
+- plain-language-summarization
+- evaluation
 ---
 
-PlainFact-summary is a high-quality human-annotated dataset designed for Plain Language Summarization tasks, along with [PlainQAFact](https://github.com/zhiwenyou103/PlainQAFact) factuality evaluation framework, as described in [PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation](https://huggingface.co/papers/2503.08890). 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)). 
+PlainFact-summary is a high-quality human-annotated dataset designed for Plain Language Summarization tasks, along with [PlainQAFact](https://github.com/zhiwenyou103/PlainQAFact) factuality evaluation framework, as described in [PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization](https://huggingface.co/papers/2503.08890). 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)). 
 In addition to using all factual plain language summaries, we also generate contrasting non-factual examples for each plain language summary. 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/)).
 
 We also provided a sentence-level version [PlainFact](https://huggingface.co/datasets/uzw/PlainFact) that split the summaries into sentences with fine-grained explanation annotations. In total, we have 200 plain language summary-abstract pairs.