将视频讲座转化为知识驱动的可视化摘要，降低学习负担。
KnowVis: Knowledge-Centric Visual Summarization for Video Lectures
- 基于多模态内容提取概念图，识别关键难点概念
- 构建结构化知识单元，生成清晰视觉摘要
- 适合教育科技、智能教学系统开发者使用
视频讲座是重要的教育资源，但其信息密集且冗长，常使初学者难以应对。根本原因在于：视频线性传递瞬时信息，而人类学习需构建互联认知网络，缺乏领域知识的新手易产生严重认知负荷。现有摘要方法多生成文字密集的线性压缩内容，仍需高认知努力。为此，我们提出KnowVis框架，将线性视频讲座转化为基于教学原理的视觉叙事。该框架首先从多模态视频内容中提取详细概念图，识别重要且具挑战性的门槛概念；随后构建结构化知识单元；最后合成吸引人的视觉摘要。我们还构建了一个包含125个跨10个学科的教育视频数据集，配套1,079个生成的视觉摘要。大量自动评估与人工研究表明，相比先进基线，KnowVis生成的视觉更准确、更清晰，有效降低认知负荷，显著提升学生学习效果与知识保留率。
原文摘要 · Abstract (English)
Video lectures are valuable educational resources, but their dense and lengthy formats often overwhelm novice learners. This difficulty stems from a fundamental pedagogical mismatch: while videos deliver transient information linearly, human learning requires constructing interconnected cognitive networks, a task that induces severe cognitive overload for novice learners lacking prior domain knowledge. Existing video summarization methods fail to resolve this mismatch, as they primarily produce text-heavy, linear condensations that still demand high cognitive effort. To bridge this gap, we propose KnowVis, a framework that transforms linear video lectures into pedagogically grounded visual narratives. KnowVis first extracts a detailed concept map from multimodal video content to identify important and challenging threshold concepts, then constructs structured knowledge units, and finally synthesizes engaging visual summaries. Alongside the framework, we introduce a curated dataset of 125 educational videos across 10 academic disciplines, paired with 1,079 generated visual summaries. Extensive automated evaluations and a human study demonstrate that, compared to state-of-the-art baselines, KnowVis generates more accurate and clear visuals that successfully reduce cognitive load and significantly improve student learning effectiveness and knowledge retention.
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