用对话框架规划摘要生成，让解释更自然准确。
Explanatory Summarization with Discourse-Driven Planning
- 基于话语结构设计摘要生成计划，引导解释性内容
- 在三个数据集上优于现有方法，减少幻觉现象
- 适合需要可控制、高可信度通俗总结的场景
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，现有自动摘要方法未显式建模解释，导致解释内容比例难以与人工摘要对齐。本文提出一种基于计划的方法，利用话语框架组织摘要生成，并通过提示响应来指导解释性句子。具体提出两种话语驱动的规划策略：计划作为输入条件或输出前缀。在三个通俗摘要数据集上的实证实验表明，该方法在摘要质量上超越现有最先进模型，同时提升模型鲁棒性、可控性并缓解幻觉问题。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。