Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts

Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present Med-EASi (underlineMedical dataset for underlineElaborative and underlineAbstractive underlineSimplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its textitexpert-layman-AI collaborative annotations facilitate textitcontrollability over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of input-output combinations, leading to two control-free and two controllable versions of the model. We add two types of textitcontrollability into text simplification, by using a multi-angle training approach: textitposition-aware, which uses in-place annotated inputs and outputs, and textitposition-agnostic, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated baseline. Furthermore, textitposition-aware control generates better simplification than the textitposition-agnostic one. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.