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 typically communicated via complex and technical articles.
This creates barriers for laypeople seeking access to up-to-date medical
findings, consequently impeding progress on health literacy. Most existing work
on medical text simplification has focused on monolingual settings, with the
result that such evidence would be available only in just one language (most
often, English). This work addresses this limitation via multilingual
simplification, i.e., directly simplifying complex texts into simplified texts
in multiple languages. We introduce MultiCochrane, the first sentence-aligned
multilingual text simplification dataset for the medical domain in four
languages: English, Spanish, French, and Farsi. We evaluate fine-tuned and
zero-shot models across these languages, with extensive human assessments and
analyses. Although models can now generate viable simplified texts, we identify
outstanding challenges that this dataset might be used to address.

本研究介绍了 MultiCochrane 数据集，并评估了跨多语言的医学简化文本模型，尽管该模型能够生成可行的简化文本，但我们也发现了仍未解决的挑战。