Robust Summarization of Doctor-Patient Conversations: TalTech Systems for the Beyond Transcription Challenge

Abstract: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 LLMs 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, as its reward. Our systems ranked first in both tracks, and an independent LLM-as-a-judge evaluation showed the lowest hallucination rate among all submissions, indicating that reinforcement learning against a concept-matching metric need not compromise factual reliability. We also find that fine-tuning on text transcripts transfers well to speech input and appears to improve robustness on out-of-domain real recordings.

Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)

arXiv:2607.17230 [cs.CL]

(or arXiv:2607.17230v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.17230 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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