  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.
