Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these models is constrained by the limited size and topical breadth of available corpora. We introduce CELLS, the largest (63k pairs) and broadest-ranging (12 journals) parallel corpus for lay language generation. The abstract and the corresponding lay language summary are written by domain experts, assuring the quality of our dataset. Furthermore, qualitative evaluation of expert-authored plain language summaries has revealed background explanation as a key strategy to increase accessibility. Such explanation is challenging for neural models to generate because it goes beyond simplification by adding content absent from the source. We derive two specialized paired corpora from CELLS to address key challenges in lay language generation: generating background explanations and simplifying the original abstract. We adopt retrieval-augmented models as an intuitive fit for the task of background explanation generation, and show improvements in summary quality and simplicity while maintaining factual correctness. Taken together, this work presents the first comprehensive study of background explanation for lay language generation, paving the path for disseminating scientific knowledge to a broader audience. CELLS is publicly available at: https://github.com/LinguisticAnomalies/pls_retrieval.

介绍了 CELLS，这是最大（63k 对）和领域最广泛（12 个期刊）的平行语料库，旨在提高健康信息可访问性。该研究通过派生两个特殊对的平行语料库，采用检索增强模型来解决简化原始摘要和生成背景解释的关键挑战，同时保持事实正确性，并通过定性评估显示了显示了质量的提高和简化的优点，为推广科学知识到更广泛的受众铺平了道路。

CELLS: 生物医学简明语生成平行语料库