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This paper describes PULSAR, our system submission at the ImageClef 2023MediQA-Sum task on summarising patient-doctor dialogues into clinical records.The proposed framework relies on domain-specific pre-training, to produce aspecialised language model which is trained on task-specific natural dataaugmented by synthetic data generated by a black-box LLM. We find limitedevidence towards the efficacy of domain-specific pre-training and dataaugmentation, while scaling up the language model yields the best performancegains. Our approach was ranked second and third among 13 submissions on task Bof the challenge. Our code is available at https://github.com/yuping-wu/PULSAR.

PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records