Summary of Real-time Speech Summarization For Medical Conversations, by Khai Le-duc et al.
Real-time Speech Summarization for Medical Conversations
by Khai Le-Duc, Khai-Nguyen Nguyen, Long Vo-Dang, Truong-Son Hy
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 a real-time speech summarization system for medical conversations, generating local and global summaries. The authors design a deployable system that enhances user experience while reducing computational costs. They also introduce the VietMed-Sum dataset, the first speech summarization dataset for medical conversations. Furthermore, they leverage large language models (LLMs) and human annotators to create gold standard and synthetic summaries. Finally, the authors provide baseline results of state-of-the-art models on VietMed-Sum. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make doctor-patient conversations more efficient by creating a system that summarizes what’s said in real-time. The authors also share a special dataset for medical conversations and show how to use big language models and human helpers to create accurate summaries. This is important because it can help people understand each other better, which is good for both the patient and the doctor. |
Keywords
» Artificial intelligence » Summarization
