Speech-to-SOAP: End-to-End Summarization of Medical Dialogues: KIT@BeTraC 2026
With the advent of Large Language Models and its instruction following capabilities a promising application is the task of summarization. Within this domain of task the extractive sub-task of clinical protocolling has emerged as a topic of particular interest as it can significantly reduce the downtime and protocolling burden of health-care workers thus enabling them to focus on their core work helping humans. A further step towards automation is the direct generation of clinical notes from speech without intermediate transcripts, reducing processing time while preserving information such as coughing or other paralinguistic cues that may be lost in transcript-based systems. To this end, w...
| Authors | Enes Yavuz Ugan, Fabian Retkowski, Yuka Ko et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 August 2026 |
| Last updated at source | 21 September 2026 |
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