Document detail
ID

oai:arXiv.org:2403.17727

Topic
Computer Science - Computer Vision... Computer Science - Computation and... Computer Science - Human-Computer ... Computer Science - Multimedia
Author
Kawamura, Kazuki Rekimoto, Jun
Category

Computer Science

Year

2024

listing date

4/3/2024

Keywords
summarization learning lecture information visual video science computer
Metrics

Abstract

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.

Kawamura, Kazuki,Rekimoto, Jun, 2024, FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts

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