Lay summaries for scientific documents typically include explanations to help readers grasp sophisticated concepts or arguments. However, current automatic summarization methods do not explicitly model explanations, which makes it difficult to align the proportion of explanatory content with human-written summaries. In this paper, we present a plan-based approach that leverages discourse frameworks to organize summary generation and guide explanatory sentences by prompting responses to the plan. Specifically, we propose two discourse-driven planning strategies, where the plan is conditioned as part of the input or part of the output prefix, respectively. Empirical experiments on three lay summarization datasets show that our approach outperforms existing state-of-the-art methods in terms of summary quality, and it enhances model robustness, controllability, and mitigates hallucination.

翻译：科学文献的通俗摘要通常包含解释性内容，以帮助读者理解复杂概念或论点。然而，当前的自动摘要方法并未显式建模解释过程，导致难以使解释性内容的比例与人工撰写的摘要相匹配。本文提出一种基于规划的方法，该方法利用篇章框架组织摘要生成，并通过提示对规划内容的响应来引导解释性句子的生成。具体而言，我们提出了两种篇章驱动的规划策略：规划分别作为输入条件或输出前缀的一部分。在三个通俗摘要数据集上的实证实验表明，我们的方法在摘要质量方面优于现有最先进方法，同时增强了模型的鲁棒性、可控性，并缓解了幻觉问题。