Slides2MindMap：从课程幻灯片重建认知高效的知识层次结构
Slides2MindMap: Reconstructing Cognitively Efficient Knowledge Hierarchies from Lecture Slides
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中文总结 AI 辅助
本研究提出Slides2MindMap任务，引入含12774张幻灯片的S2M-Bench基准，提出AutoMindMap智能体框架，实验证明其在基准上优于基线且鲁棒性强，具备教学应用价值。
中文摘要 AI 辅助
从课程幻灯片生成思维导图可帮助学习者高效吸收碎片化知识，对智能教育具有重要价值，但专门的自动生成与评估框架仍有待探索且颇具挑战，需兼顾全局-局部知识焦点平衡并处理大规模、异质性幻灯片。我们提出Slides2MindMap任务，旨在从课程的幻灯片集合中重建认知高效的知识层次结构。为进行系统评估，我们引入S2M-Bench基准，其包含12774张幻灯片页面，对应24门大学课程的专家标注思维导图。S2M-Bench还包含基于认知科学的评估框架，整合了基于真值的比较、结构一致性分析以及VLM-as-a-Judge。为解决该任务，我们提出AutoMindMap，一个受结构构建框架（Structure Building Framework）启发的智能体框架，包含用于全局支架锚定的骨架铺设（Skeleton Laying）、由上下文感知摘要增强的迭代知识整合，以及带有局部-全局解耦机制的双阶段优化。该框架协调了局部知识保真度与全局一致性，并适配幻灯片特定特征。在S2M-Bench上的实验表明，AutoMindMap在不同模型和场景下均优于基线方法且具备出色的鲁棒性，凸显了其在教学中的应用价值。
英文摘要
Generating mind maps from lecture slides can help learners efficiently assimilate fragmented knowledge, promising substantial benefits for intelligent education. However, dedicated automatic generation and evaluation frameworks remain underexplored and challenging, requiring a global-local knowledge focus balance and handling large-scale, heterogeneous slides. We formulate the Slides2MindMap task, which aims to reconstruct cognitively efficient knowledge hierarchies from a course's slide deck collection. For systematic evaluation, we introduce S2M-Bench, a benchmark comprising 12,774 slide pages with expert-annotated mind maps spanning 24 university courses. S2M-Bench includes a cognitive-science-grounded evaluation framework that integrates ground-truth-based comparison, structure conformity analysis, and VLM-as-a-Judge. To address this task, we propose AutoMindMap, an agentic framework inspired by the Structure Building Framework. AutoMindMap comprises Skeleton Laying for global scaffold anchoring, Iterative Knowledge Integration augmented by context-aware summarization, and Dual-Stage Refinement with a local-global decoupling mechanism. The framework reconciles local knowledge faithfulness with global coherence, and adapts to slide-specific features. Experiments on S2M-Bench demonstrate that AutoMindMap outperforms baselines and achieves superior robustness across different models and scenarios, underscoring its pedagogical application value.
