# Speech-to-SOAP: End-to-End Summarization of Medical Dialogues: KIT@BeTraC 2026

arXiv: https://arxiv.org/abs/2608.24327 · HTML: https://mlsanity.com/paper/2608.24327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models](https://mlsanity.com/paper/2306.07691/) (2023)
- [ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation](https://mlsanity.com/paper/2306.02022/) (2023)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [PriMock57: A Dataset Of Primary Care Mock Consultations](https://mlsanity.com/paper/2204.00333/) (2022)
- [Efficient Weight factorization for Multilingual Speech Recognition](https://mlsanity.com/paper/2105.03010/) (2021)
- [Rehearsal-Free Online Continual Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2306.10860/) (2023)
- [Weight Factorization and Centralization for Continual Learning in Speech Recognition](https://mlsanity.com/paper/2506.16574/) (2025)
- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization](https://mlsanity.com/paper/2604.06138/) (2026)
- [Multilingual Long-Form Speech Instruction Following: KIT's Submission to IWSLT 2026](https://mlsanity.com/paper/2606.04730/) (2026)

## Abstract (arXiv)

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, we present KIT's submission to this years BeTraC challenge in the lightweight track. Our main contribution is a scalable data augmentation pipeline that unifies heterogeneous medical dialogue datasets through synthetic speech generation and automatically generated SOAP supervision, enabling robust adaptation of a speech foundation model for end-to-end speech-to-SOAP generation.