Robust Summarization of Doctor-Patient Conversations: TalTech Systems for the Beyond Transcription Challenge
Quick Answer
This paper shows that TalTech's Voxtral Mini and Small models excelled in the Beyond Transcription Challenge, generating SOAP notes from doctor-patient conversations without transcription.
Quick Take
They achieved first place in both tracks, demonstrating low hallucination rates and improved robustness through fine-tuning and reinforcement learning.
Key Points
- Voxtral Mini and Small adapted with LoRA fine-tuning and DAPO reinforcement learning.
- Ranked first in both lightweight and heavyweight tracks of BeTraC.
- Achieved the lowest hallucination rate among all submissions.
- Fine-tuning on text transcripts improved robustness for speech input.
- Utilized Open Medical Concept F1 as a challenge metric.
Paper Resources
Source Excerpt
This paper describes TalTech's submissions to the Beyond Transcription Challenge (BeTraC), which requires generating SOAP notes directly from long doctor-patient conversation recordings, without intermediate transcription. After screening open-weight speech for long-audio robustness, we adapted Voxtral Mini (lightweight track) and Voxtral Small (heavyweight track) with LoRA supervised fine-tuning followed by DAPO reinforcement learning that uses the challenge metric, Open Medical Concept F1
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