English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Text simplification has emerged as an increasingly useful application of AIfor bridging the communication gap in specialized fields such as medicine,where the lexicon is often dominated by technical jargon and complexconstructs. Despite notable progress, methods in medical simplificationsometimes result in the generated text having lower quality and diversity. Inthis work, we explore ways to further improve the readability of textsimplification in the medical domain. We propose (1) a new unlikelihood lossthat encourages generation of simpler terms and (2) a reranked beam searchdecoding method that optimizes for simplicity, which achieve better performanceon readability metrics on three datasets. This study's findings offer promisingavenues for improving text simplification in the medical field.

Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding