A survey of automated methods for biomedical text simplification.

Though neural methods hold promise for this task, scarcity of parallel data has led to continued development of procedural methods. Various low-resource mitigations have been proposed to advance neural methods, including paragraph-level and unsupervised models and augmentation of neural models with procedural elements drawing from knowledge bases. However, high-quality parallel data will likely be crucial for developing fully automated biomedical text simplification.