Data Augmentation for Radiology Report Simplification
Ziyu Yang, Santhosh Cherian, Slobodan Vučetić · 2023
This work considers the development of a text simplification model to help patients better understand their radiology reports.This paper proposes a data augmentation approach to address the data scarcity issue caused by the high cost of manual simplification.It prompts a large foundational pre-trained language model to generate simplifications of unlabeled radiology sentences.In addition, it uses paraphrasing of labeled radiology sentences.Experimental results show that the proposed data augmentation approach enables the training of a significantly more accurate simplification model than the baselines.