Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e.g., researchers), is unlikely to explicitly define all technical concepts or state all of the background information that is relevant for a lay audience. We address this issue by augmenting eLife, an existing biomedical lay summarisation dataset, with article-specific knowledge graphs, each containing detailed information on relevant biomedical concepts. Using both automatic and human evaluations, we systematically investigate the effectiveness of three different approaches for incorporating knowledge graphs within lay summarisation models, with each method targeting a distinct area of the encoder-decoder model architecture. Our results confirm that integrating graph-based domain knowledge can significantly benefit lay summarisation by substantially increasing the readability of generated text and improving the explanation of technical concepts.

翻译：先前的自动通俗摘要生成方法完全依赖源文章，而源文章面向技术受众（如研究人员）撰写，不太可能明确定义所有技术概念或陈述与普通受众相关的全部背景信息。我们通过为现有生物医学通俗摘要数据集eLife补充文章级知识图谱来解决这一问题，每个知识图谱包含相关生物医学概念的详细信息。通过自动评估与人工评估，我们系统研究了三种将知识图谱融入通俗摘要模型的方法的有效性，每种方法针对编码器-解码器模型架构的不同区域。实验结果证实，整合基于图的领域知识可通过显著提升生成文本的可读性及改善技术概念解释，有效促进通俗摘要生成。