Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature. Deep Learning algorithms for automatic adaptation are a possible solution; however, gold standard datasets are needed for proper evaluation. Proposed datasets thus far consist of either pairs of comparable professional- and general public-facing documents or pairs of semantically similar sentences mined from such documents. This leads to a trade-off between imperfect alignments and small test sets. To address this issue, we created the Plain Language Adaptation of Biomedical Abstracts dataset. This dataset is the first manually adapted dataset that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we benchmark automatic adaptation on the dataset with state-of-the-art Deep Learning approaches, setting baselines for future research.

为了让公众可靠理解庞大的医学文献，本研究提出了通过深度学习算法自动适应平民语言版本的解决方案，同时为了保证适应的准确性，首先需要建立一个金标准数据集，而为了解决目前现有的数据集相互不对齐及小试验集的问题，本研究建立了第一个均已手动适配的数据集，并使用最新的深度学习方法进行了基准测试。

医学摘要普通语言适应数据集