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

• URL: http://arxiv.org/abs/2302.09155v1
• Date: Fri, 17 Feb 2023 21:50:13 GMT
• Title: Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts
• Authors: Chandrayee Basu, Rosni Vasu, Michihiro Yasunaga, Qian Yang
• Abstract summary: Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible. We present $textbfMed-EASi$ ($underlinetextbfMed$ical dataset for $underlinetextbfE$laborative and $underlinetextbfA$bstractive $underlinetextbfSi$mplification) Our results show that our fine-grained annotations improve learning compared to the unannotated baseline.
• Score: 32.57058284812338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\textbf{Med-EASi}$ ($\underline{\textbf{Med}}$ical dataset for $\underline{\textbf{E}}$laborative and $\underline{\textbf{A}}$bstractive $\underline{\textbf{Si}}$mplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its $\textit{expert-layman-AI collaborative}$ annotations facilitate $\textit{controllability}$ 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 $\textit{controllability}$ into text simplification, by using a multi-angle training approach: $\textit{position-aware}$, which uses in-place annotated inputs and outputs, and $\textit{position-agnostic}$, 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, $\textit{position-aware}$ control generates better simplification than the $\textit{position-agnostic}$ one. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.

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