# MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues

arXiv: https://arxiv.org/abs/2408.14418 · HTML: https://mlsanity.com/paper/2408.14418/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Adversarial Training for Large Neural Language Models](https://mlsanity.com/paper/2004.08994/) (2020)
- [SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation](https://mlsanity.com/paper/1808.07512/) (2018)
- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting](https://mlsanity.com/paper/2309.15649/) (2023)
- [Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques](https://mlsanity.com/paper/2005.01795/) (2020)
- [Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition](https://mlsanity.com/paper/2310.06434/) (2023)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [Controllable Dialogue Simulation with In-Context Learning](https://mlsanity.com/paper/2210.04185/) (2022)
- [Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors](https://mlsanity.com/paper/2006.05635/) (2020)
- [Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks](https://mlsanity.com/paper/2401.02921/) (2024)
- [UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction](https://mlsanity.com/paper/2401.05689/) (2024)

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare](https://mlsanity.com/paper/2502.15871/) (2025)
- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [Evaluating Differentially Private Generation of Domain-Specific Text](https://mlsanity.com/paper/2508.20452/) (2025)
- [A Typology of Synthetic Datasets for Dialogue Processing in Clinical Contexts](https://mlsanity.com/paper/2505.03025/) (2025)
- [Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation](https://mlsanity.com/paper/2508.18210/) (2025)
- [PiDA: Phonetically-Informed Data Augmentation for Robust Vietnamese Speech Translation](https://mlsanity.com/paper/2606.12911/) (2026)
- [Should We Type or Talk to LLM Agents? A Comprehensive Study of Voice and Keyboard Input Perturbations](https://mlsanity.com/paper/2608.03970/) (2026)
- [Curriculum-Based Noise Adaptation for Phoneme-to-Text Reconstruction in Visual Speech Recognition](https://mlsanity.com/paper/2609.20839/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks like summarization. This issue is particularly pronounced in clinical dialogue summarization, a low-resource domain where supervised data for fine-tuning is scarce, necessitating the use of ASR models as black-box solutions. Employing conventional data augmentation for enhancing the noise robustness of summarization models is not feasible either due to the unavailability of sufficient medical dialogue audio recordings and corresponding ASR transcripts. To address this challenge, we propose MEDSAGE, an approach for generating synthetic samples for data augmentation using Large Language Models (LLMs). Specifically, we leverage the in-context learning capabilities of LLMs and instruct them to generate ASR-like errors based on a few available medical dialogue examples with audio recordings. Experimental results show that LLMs can effectively model ASR noise, and incorporating this noisy data into the training process significantly improves the robustness and accuracy of medical dialogue summarization systems. This approach addresses the challenges of noisy ASR outputs in critical applications, offering a robust solution to enhance the reliability of clinical dialogue summarization.