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.

通过使用特定于每篇文章的知识图谱来增加生物医学概念的详细信息，我们系统地调查了三种不同方法在提高自动化简报摘要模型效果方面的有效性，结果确认了整合基于图的领域知识可以显著改善生成文本的可读性和解释技术概念的能力。

利用外部知识图谱增强生物医学术语摘要