KnowVis: Knowledge-Centric Visual Summarization for Video Lectures

Abstract: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.

This work is published on EMNLP 2026 (Findings). Our code and dataset are available at this https URL and this https URL

Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)

arXiv:2609.03742 [cs.CV]

(or arXiv:2609.03742v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.03742 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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