| --- |
| language: en |
| tags: |
| - summarization |
| - medical |
| library_name: transformers |
| pipeline_tag: summarization |
| --- |
| |
| # Automatic Personalized Impression Generation for PET Reports Using Large Language Models 📄✍ |
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| **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 |
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| [Read the full paper](https://arxiv.org/abs/2309.10066) |
| <!-- Link to our Arxiv paper --> |
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| ## 📑 Model Description |
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| This is the domain-adapted BARTScore for evaluating the quality of PET impressions. |
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| To check our domain-adapted text-generation-based evaluation metrics: |
| - [BARTScore+PET](https://huggingface.co/xtie/BARTScore-PET) |
| - [PEGASUSScore+PET](https://huggingface.co/xtie/PEGASUSScore-PET) |
| - [T5+PET](https://huggingface.co/xtie/T5Score-PET) |
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| ## 🚀 Usage |
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| Clone this GitHub repository in a local folder |
| ```bash |
| git clone https://github.com/xtie97/PET-Report-Summarization.git |
| ``` |
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| Go the the folder containing codes for computing BARTScore and create a new folder called "checkpoints" |
| ```bash |
| cd ./PET-Report-Summarization/evaluation_metrics/metrics/BARTScore |
| mkdir checkpoints |
| mkdir checkpoints/bart-large |
| ``` |
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| Download the model weights and put them in the folder "checkpoints/bart-large". Run the code for computing text-generation-based metrics |
| ``` |
| python compute_metrics_text_generation.py |
| ``` |
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| ## 📁 Additional Resources |
| - **Codebase for evaluation metrics:** [GitHub](https://github.com/xtie97/PET-Report-Summarization/tree/main/evaluation_metrics) |
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