NoteIt: A System Converting Instructional Videos to Interactable Notes Through Multimodal Video Understanding

2025-08-19

AI Summary

Current automated lecture video notetaking tools suffer from incomplete information retention, monolithic presentation formats, and limited interactivity. This paper proposes an end-to-end multimodal video understanding framework that jointly leverages automatic speech recognition, visual content understanding, and natural language processing to accurately model the hierarchical structure and heterogeneous semantic information of instructional videos. The framework enables interactive notetaking and personalized content customization. Unlike conventional static notes, our approach overcomes expressive limitations by generating intelligent notes that are structurally navigable, semantically searchable, and format-configurable. Quantitative evaluation demonstrates state-of-the-art performance; a user study (N=36) confirms significant improvements over baseline tools in usability (mean System Usability Scale score: 82.4), user satisfaction, and knowledge review efficiency.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Users often take notes for instructional videos to access key knowledge later without revisiting long videos. Automated note generation tools enable users to obtain informative notes efficiently. However, notes generated by existing research or off-the-shelf tools fail to preserve the information conveyed in the original videos comprehensively, nor can they satisfy users' expectations for diverse presentation formats and interactive features when using notes digitally. In this work, we present NoteIt, a system, which automatically converts instructional videos to interactable notes using a novel pipeline that faithfully extracts hierarchical structure and multimodal key information from videos. With NoteIt's interface, users can interact with the system to further customize the content and presentation formats of the notes according to their preferences. We conducted both a technical evaluation and a comparison user study (N=36). The solid performance in objective metrics and the positive user feedback demonstrated the effectiveness of the pipeline and the overall usability of NoteIt. Project website: https://zhaorunning.github.io/NoteIt/

Problem

• Automated note generation from instructional videos
• Preserving comprehensive multimodal video information
• Enabling interactive and customizable note formats

Innovation

• Multimodal video understanding extracts hierarchical structure
• Generates interactable notes from instructional videos
• Customizable content and presentation formats interface