JMIR Publications

ABSTRACT

Background:

In most cases, the abstracts of articles in the medical domain are publicly available. Although these are accessible by everyone, they are hard to comprehend for a wider audience due to the complex medical vocabulary. Thus, simplifying these complex abstracts is essential to make medical research accessible to the general public.

Objective:

This paper aims to develop a deep learning model that converts complex medical text to a simpler version while maintaining the quality of the generated text.

Methods:

A text simplification approach using Reinforcement Learning and Transformer-based language models was developed. Relevance reward, Flesch Kincaid reward and Lexical Simplicity reward were optimized to help simplify jargon-dense complex medical paragraphs to their simpler versions while retaining the quality of the text. The model was trained using 3,568 complex-simple medical paragraphs and evaluated on 480 paragraphs via the help of automated metrics and human annotation.

Results:

The proposed method outperformed previous baselines on Flesch Kincaid Scores (11.84) and achieved comparable performance to other baselines when measured using ROUGE-1 (0.39), ROUGE-2 (0.11) and SARI scores (0.40). Manual evaluation showed that percent agreement between human annotators was more than 70% when factors like fluency, coherence and adequacy were considered.

Conclusions:

A unique medical text simplification approach is successfully developed that leverages reinforcement learning and accurately simplifies complex medical paragraphs, hence increasing their readability.