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。

医学文本的多语言简化

Multilingual Simplification of Medical Texts

Automated text simplification, a technique useful for making text more
accessible to people such as children and emergent bilinguals, is often thought
of as a monolingual translation task from complex sentences to simplified
sentences using encoder-decoder models. This view fails to account for
elaborative simplification, where new information is added into the simplified
text. This paper proposes to view elaborative simplification through the lens
of the Question Under Discussion (QUD) framework, providing a robust way to
investigate what writers elaborate upon, how they elaborate, and how
elaborations fit into the discourse context by viewing elaborations as explicit
answers to implicit questions. We introduce ElabQUD, consisting of 1.3K
elaborations accompanied with implicit QUDs, to study these phenomena. We show
that explicitly modeling QUD (via question generation) not only provides
essential understanding of elaborative simplification and how the elaborations
connect with the rest of the discourse, but also substantially improves the
quality of elaboration generation.

研究讨论将拓展式简化与 QUD 框架相结合，通过问题生成对其进行明确建模，以提高拓展式简化的质量和理解。

阐述简化作为讨论中的隐式问题

Elaborative Simplification as Implicit Questions Under Discussion

Accessing medical literature is difficult for laypeople as the content is
written for specialists and contains medical jargon. Automated text
simplification methods offer a potential means to address this issue. In this
work, we propose a summarize-then-simplify two-stage strategy, which we call
NapSS, identifying the relevant content to simplify while ensuring that the
original narrative flow is preserved. In this approach, we first generate
reference summaries via sentence matching between the original and the
simplified abstracts. These summaries are then used to train an extractive
summarizer, learning the most relevant content to be simplified. Then, to
ensure the narrative consistency of the simplified text, we synthesize
auxiliary narrative prompts combining key phrases derived from the syntactical
analyses of the original text. Our model achieves results significantly better
than the seq2seq baseline on an English medical corpus, yielding 3%~4% absolute
improvements in terms of lexical similarity, and providing a further 1.1%
improvement of SARI score when combined with the baseline. We also highlight
shortcomings of existing evaluation methods, and introduce new metrics that
take into account both lexical and high-level semantic similarity. A human
evaluation conducted on a random sample of the test set further establishes the
effectiveness of the proposed approach. Codes and models are released here:
this https URL.

该研究提出了一种名为 NapSS 的自动文本简化方法，采用基于句子匹配的摘要生成和提取式摘要相结合的策略来帮助医学领域的非专业人士更好地理解专业文献，相对于基准策略，在保持原始叙述连贯性的同时提高了文本简化的词汇和语义相似性，实验结果表明该方法可以更有效地提高非专业人士的阅读效率。