add paper link/doi

README.md

@@ -49,7 +49,7 @@ library_name: transformers
 </a>
 
 ## TL;DR
-**medqsum-bart-large-xsum-meqsum** is the best fine-tuned model in the paper [Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach](), which introduces a solution to get the most out of LLMs, when answering health-related questions. We address the challenge of crafting accurate prompts by summarizing consumer health questions (CHQs) to generate clear and concise medical questions. Our approach involves fine-tuning Transformer-based models, including Flan-T5 in resource-constrained environments and three medical question summarization datasets.
+**medqsum-bart-large-xsum-meqsum** is the best fine-tuned model in the paper [Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach](https://doi.org/10.1109/SITA60746.2023.10373720), which introduces a solution to get the most out of LLMs, when answering health-related questions. We address the challenge of crafting accurate prompts by summarizing consumer health questions (CHQs) to generate clear and concise medical questions. Our approach involves fine-tuning Transformer-based models, including Flan-T5 in resource-constrained environments and three medical question summarization datasets.
 
 ## Hyperparameters
 ```json