Update README.md

README.md

@@ -8,11 +8,7 @@ pretty_name: PubMedAbstractSubset
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 # PubMed Abstracts Subset (10%)
 
-This dataset contains a 10% probabilistic sample of the ~24 million PubMed abstracts available as public metadata from the [National Library of Medicine (NLM)](https://pubmed.ncbi.nlm.nih.gov/). The dataset was originally compiled and released as part of the [MedRAG benchmark](https://arxiv.org/abs/2402.13178), and has been reformatted and republished as part of the study:
-
-**Stuhlmann et al. (2025)**  
-*Efficient and Reproducible Biomedical Question Answering using Retrieval‑Augmented Generation*  
-→ [arXiv:2505.07917](https://arxiv.org/abs/2505.07917)
+This dataset contains a probabilistic sample of all publicly available PubMed metadata from the [National Library of Medicine (NLM)](https://pubmed.ncbi.nlm.nih.gov/).
 
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@@ -77,30 +73,14 @@ Each file is in `.jsonl` (JSON Lines) format, where each line is a valid JSON ob
 
 This dataset is derived from public domain PubMed metadata (titles and abstracts), redistributed in accordance with [NLM data usage policies](https://www.nlm.nih.gov/databases/download/data_distrib_main.html).
   
-- Reformatted and used in:  
+- Used in:  
   **Stuhlmann et al. (2025)**, *Efficient and Reproducible Biomedical QA using RAG*, [arXiv:2505.07917](https://arxiv.org/abs/2505.07917)
 
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-## ✨ Citation
-
-If you use this dataset, please cite:
-
-```
-
-@article{stuhlmann2025efficient,
-  title={Efficient and Reproducible Biomedical Question Answering using Retrieval-Augmented Generation},
-  author={Stuhlmann, Linus and Saxer, Michael Alexander and Fürst, Jonathan},
-  journal={arXiv preprint arXiv:2505.07917},
-  year={2025}
-}
-```
-
----
-
 ## 🏷️ Version
 
-- `v1.0` – Initial release (10% sample, 2.39M entries, 24 JSONL files)
+- `v1.0` – Initial release (2.39M entries, 24 JSONL files)
 
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