mlsanityData to 7 Oct 2026

FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts

arXiv 2403.17727 ↗First posted 26 Mar 2024cs.CV cs.CL cs.HCcited by 1

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Abstract (arXiv)

Quickly understanding lengthy lecture videos is essential for learners with limited time and interest in various topics to improve their learning efficiency. To this end, video summarization has been actively researched to enable users to view only important scenes from a video. However, these studies focus on either the visual or audio information of a video and extract important segments in the video. Therefore, there is a risk of missing important information when both the teacher's speech and visual information on the blackboard or slides are important, such as in a lecture video. To tackle this issue, we propose FastPerson, a video summarization approach that considers both the visual and auditory information in lecture videos. FastPerson creates summary videos by utilizing audio transcriptions along with on-screen images and text, minimizing the risk of overlooking crucial information for learners. Further, it provides a feature that allows learners to switch between the summary and original videos for each chapter of the video, enabling them to adjust the pace of learning based on their interests and level of understanding. We conducted an evaluation with 40 participants to assess the effectiveness of our method and confirmed that it reduced viewing time by 53% at the same level of comprehension as that when using traditional video playback methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 71 references that are in the map. First posted on arXiv 26 Mar 2024.

Builds on

20 of 71 references.

References · 20

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8 more
  1. 2017Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Rewardcited by 105
  2. 2017Deep Voice: Real-time Neural Text-to-Speechcited by 83
  3. 2019Hierarchical Recurrent Neural Network for Video Summarizationcited by 44
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