Automated lay summarisation (LS) aims to simplify complex technical documents
into a more accessible format to non-experts. Existing approaches using
pre-trained language models, possibly augmented with external background
knowledge, tend to struggle with effective simplification and explanation.
Moreover, automated methods that can effectively assess the `layness' of
generated summaries are lacking. Recently, large language models (LLMs) have
demonstrated a remarkable capacity for text simplification, background
information generation, and text evaluation. This has motivated our systematic
exploration into using LLMs to generate and evaluate lay summaries of
biomedical articles. We propose a novel \textit{Explain-then-Summarise} LS
framework, which leverages LLMs to generate high-quality background knowledge
to improve supervised LS. We also evaluate the performance of LLMs for
zero-shot LS and propose two novel LLM-based LS evaluation metrics, which
assess layness from multiple perspectives. Finally, we conduct a human
assessment of generated lay summaries. Our experiments reveal that
LLM-generated background information can support improved supervised LS.
Furthermore, our novel zero-shot LS evaluation metric demonstrates a high
degree of alignment with human preferences. We conclude that LLMs have an
important part to play in improving both the performance and evaluation of LS
methods.

使用大型语言模型改进自动化文档摘要，尤其在简化复杂技术文件、生成背景知识、评估文本中起到了重要作用，并在医学文章的简化和评估方面做出了创新性的工作。