Text Simplification of Scientific Texts for Non-Expert Readers

TL;DR

This paper explores methods for simplifying scientific texts, especially abstracts, to make them accessible to non-expert readers, using various summarization models and ChatGPT.

Contribution

It introduces three baseline runs with existing summarization models and one with ChatGPT for scientific abstract simplification.

Findings

Effective simplification of scientific abstracts demonstrated

Out-of-the-box models provide a solid baseline

ChatGPT offers promising results for complex phrase identification

Abstract

Reading levels are highly individual and can depend on a text's language, a person's cognitive abilities, or knowledge on a topic. Text simplification is the task of rephrasing a text to better cater to the abilities of a specific target reader group. Simplification of scientific abstracts helps non-experts to access the core information by bypassing formulations that require domain or expert knowledge. This is especially relevant for, e.g., cancer patients reading about novel treatment options. The SimpleText lab hosts the simplification of scientific abstracts for non-experts (Task 3) to advance this field. We contribute three runs employing out-of-the-box summarization models (two based on T5, one based on PEGASUS) and one run using ChatGPT with complex phrase identification.