JEBS: A Fine-grained Biomedical Lexical Simplification Task

TL;DR

This paper introduces JEBS, a detailed biomedical lexical simplification task and dataset, enabling targeted development of systems to simplify complex medical jargon for the general public.

Contribution

It presents a novel fine-grained lexical simplification task, a large dataset with annotations, and baseline results, advancing biomedical text simplification research.

Findings

Dataset contains 21,595 replacements for 10,314 terms.

Baseline results for rule-based and transformer systems are provided.

JEBS enables targeted evaluation of biomedical lexical simplification.

Abstract

Online medical literature has made health information more available than ever, however, the barrier of complex medical jargon prevents the general public from understanding it. Though parallel and comparable corpora for Biomedical Text Simplification have been introduced, these conflate the many syntactic and lexical operations involved in simplification. To enable more targeted development and evaluation, we present a fine-grained lexical simplification task and dataset, Jargon Explanations for Biomedical Simplification (JEBS, https://github.com/bill-from-ri/JEBS-data ). The JEBS task involves identifying complex terms, classifying how to replace them, and generating replacement text. The JEBS dataset contains 21,595 replacements for 10,314 terms across 400 biomedical abstracts and their manually simplified versions. Additionally, we provide baseline results for a variety of rule-based and transformer-based systems for the three sub-tasks. The JEBS task, data, and baseline results pave the way for development and rigorous evaluation of systems for replacing or explaining complex biomedical terms.