4 citations · 4 across the 1 of their papers we have counts for
3 papers
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
Jan Trienes, Sebastian Joseph, Jörg Schlötterer +5
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework…
Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)
Chantal Shaib, Millicent L. Li, Sebastian Joseph +3
Large language models, particularly GPT-3, are able to produce high quality summaries of general domain news articles in few- and zero-shot settings. However, it is unclear if such…
Multilingual Simplification of Medical Texts
Sebastian Joseph, Kathryn Kazanas, Keziah Reina +4
Automated text simplification aims to produce simple versions of complex texts. This task is especially useful in the medical domain, where the latest medical findings are typicall…