A text style transfer system for reducing the physician–patient expertise gap: An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases. •Development of Text Style Transfer system to improve physician–patient communication.•Development of Semantic Textual Similarity methods for collecting parallel datasets.•Evaluation of the impact of collected parallel data on the Text Style Transfer.•Performance analysis based on physicians’ evaluation.•A new dataset for Semantic Similarity and Style Transfer in the medical domain.