  The goal of text simplification (TS) is to transform difficult text into a
version that is easier to understand and more broadly accessible to a wide
variety of readers. In some domains, such as healthcare, fully automated
approaches cannot be used since information must be accurately preserved.
Instead, semi-automated approaches can be used that assist a human writer in
simplifying text faster and at a higher quality. In this paper, we examine the
application of autocomplete to text simplification in the medical domain. We
introduce a new parallel medical data set consisting of aligned English
Wikipedia with Simple English Wikipedia sentences and examine the application
of pretrained neural language models (PNLMs) on this dataset. We compare four
PNLMs(BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of
the sentence to be simplified can be incorporated to achieve better results
(6.17% absolute improvement over the best individual model). We also introduce
an ensemble model that combines the four PNLMs and outperforms the best
individual model by 2.1%, resulting in an overall word prediction accuracy of
64.52%.
