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.
@article{arxiv.2310.11191,
title = {Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding},
author = {Lorenzo Jaime Yu Flores and Heyuan Huang and Kejian Shi and Sophie Chheang and Arman Cohan},
journal= {arXiv preprint arXiv:2310.11191},
year = {2023}
}