  Text simplification has emerged as an increasingly useful application of AI
for bridging the communication gap in specialized fields such as medicine,
where the lexicon is often dominated by technical jargon and complex
constructs. Despite notable progress, methods in medical simplification
sometimes result in the generated text having lower quality and diversity. In
this work, we explore ways to further improve the readability of text
simplification in the medical domain. We propose (1) a new unlikelihood loss
that encourages generation of simpler terms and (2) a reranked beam search
decoding method that optimizes for simplicity, which achieve better performance
on readability metrics on three datasets. This study's findings offer promising
avenues for improving text simplification in the medical field.

Computation and Language;Artificial Intelligence