Summary of Optimal Path For Biomedical Text Summarization Using Pointer Gpt, by Hyunkyung Han et al.
Optimal path for Biomedical Text Summarization Using Pointer GPT
by Hyunkyung Han, Jaesik Choi
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to biomedical text summarization, addressing limitations in traditional transformer models like GPT. The researchers replaced the attention mechanism in GPT with a pointer network, designed to preserve original text values during summarization. They evaluated their Pointer-GPT model using the ROUGE score, finding it outperformed the original GPT model. This breakthrough has implications for integrating accurate and informative summaries into Electronic Medical Records (EMR) systems, revolutionizing clinicians’ interactions with patient medical records. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier for doctors to get a quick summary of patients’ health information from long documents. Right now, computers use special models called transformers to do this job, but they can make mistakes and leave out important details. To fix this, the researchers changed one part of the model, called the attention mechanism, to something called a pointer network. This new model is better at keeping the original text’s meaning during summarization. The results showed that it works even better than the old model! This could lead to big changes in how doctors use computers to look at patients’ medical records. |
Keywords
» Artificial intelligence » Attention » Gpt » Rouge » Summarization » Transformer
