Update README.md

README.md

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 # Automatic Personalized Impression Generation for PET Reports Using Large Language Models 📄✍
 
-<p align="center">
-  <a href="https://arxiv.org/pdf/1910.10683.pdf"><img src="https://img.shields.io/badge/Paper-ArXiv-blue.svg"></a>
-  <a href="https://github.com/xtie97/PET-Report-Summarization"><img src="https://img.shields.io/badge/GitHub-Repo-green.svg"></a>
-</p>
-
 **Authored by**: Xin Tie, Muheon Shin, Ali Pirasteh, Nevein Ibrahim, Zachary Huemann, Sharon M. Castellino, Kara Kelly, John Garrett, Junjie Hu, Steve Y. Cho, Tyler J. Bradshaw
 
-### Abstract
+## 📑 Abstract
 
 Purpose: To determine if fine-tuned large language models (LLMs) can generate accurate, personalized impressions for whole-body PET reports. 
 
@@ -19,31 +14,33 @@ Conclusion: Personalized impressions generated by PEGASUS were clinically useful
 
 [Read the full paper](https://arxiv.org/pdf/1910.10683.pdf)
 
-### Uses
+## 🚀 Usage
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
-#### Metrics (for detailed evaluation results, please reference our paper):
-- ROUGE-1: 1
-- ROUGE-2: 2
-- ROUGE-L: 3
-- BLEU: 4
-- CHRF: 5
-- BERTScore: 6
+### 📊 Performance Metrics
+
+For an exhaustive evaluation, please refer to our [paper](https://arxiv.org/pdf/1910.10683.pdf):
+- **ROUGE-1**: 1
+- **ROUGE-2**: 2
+- **ROUGE-L**: 3
+- **BLEU**: 4
+- **CHRF**: 5
+- **BERTScore**: 6
 
-#### Summary
-The fine-tuned large language model provides clinically useful, personalized impressions based on PET findings. To our knowledge, this is the first attempt to automate impression generation for whole-body PET reports. 
+### 💡 Highlights
 
-#### Hardware
-NVIDIA A100 GPUs
+- The fine-tuned large language model provides clinically useful, personalized impressions based on PET findings.
+- To our knowledge, this is the first attempt to automate impression generation for whole-body PET reports. 
 
-#### Additional Information
-- **Finetuned from model:** https://huggingface.co/google/t5-v1_1-large
-- 
-- **Repository:** [https://github.com/xtie97/PET-Report-Summarization]
+### 🖥️ Hardware
 
-## Citation 
+The models were trained on NVIDIA A100 GPUs.
 
+---
 
+## 📁 Additional Resources
+- **Finetuned from model:** [Facebook's BART Large) (https://huggingface.co/google/t5-v1_1-large)
+- **Repository:** [GitHub Repository](https://github.com/xtie97/PET-Report-Summarization)