医疗文本的多语言简化
Multilingual Simplification of Medical Texts
AI总结：
针对医疗文本单语简化的局限，本文提出多语言简化方法，并构建了首个涵盖四种语言的医疗领域多语言文本简化数据集 MultiCochrane，通过评估微调与零样本模型验证了其有效性并指出未来挑战。
摘要：
自动文本简化旨在生成复杂文本的简单版本。该任务在医疗领域尤为有用，因为最新的医学发现通常通过复杂且技术性强的文章进行交流。这为寻求获取最新医学发现的非专业人士设置了障碍，进而阻碍了健康素养的提升。现有关于医疗文本简化的工作大多集中在单语环境，导致此类信息仅以一种语言（通常是英语）提供。本研究通过多语言简化来解决这一局限性，即将复杂文本直接简化为多种语言的简化文本。我们引入了 MultiCochrane，这是首个面向医疗领域的句子对齐多语言文本简化数据集，包含四种语言：英语、西班牙语、法语和波斯语。我们评估了这些语言上的微调和零样本模型，并进行了广泛的人工评估与分析。尽管模型现在能够生成可行的简化文本，我们仍指出了该数据集可用于解决的突出挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
