Text Simplification of Scientific Texts for Non-Expert Readers
This work addresses making scientific information accessible to non-experts, but it is incremental as it applies existing methods to a new dataset without novel contributions.
The paper tackled simplifying scientific abstracts for non-expert readers, such as cancer patients, by using out-of-the-box summarization models and ChatGPT with phrase identification, but did not report concrete performance numbers or results.
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.