Update task categories for PlainFact dataset

README.md

@@ -5,6 +5,8 @@ license: cc-by-sa-3.0
 size_categories:
 - 1K<n<10K
 task_categories:
+- question-answering
+- text-classification
 - summarization
 tags:
 - biomedical
@@ -15,7 +17,7 @@ tags:
 - factuality
 ---
 
-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)). 
+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)).
 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).
 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/)).
 
@@ -41,12 +43,12 @@ Citation
 If you use data from PlainFact or PlainFact-summary, please cite with the following BibTex entry:
 ```
 @misc{you2025plainqafactautomaticfactualityevaluation,
-      title={PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization}, 
+      title={PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization},
       author={Zhiwen You and Yue Guo},
       year={2025},
       eprint={2503.08890},
       archivePrefix={arXiv},
       primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2503.08890}, 
+      url={https://arxiv.org/abs/2503.08890},
 }
 ```