FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909151110627328 |
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| author | Kawamura, Kazuki Rekimoto, Jun |
| author_facet | Kawamura, Kazuki Rekimoto, Jun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_17727 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts Kawamura, Kazuki Rekimoto, Jun Computer Vision and Pattern Recognition Computation and Language Human-Computer Interaction Multimedia 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. |
| title | FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts |
| topic | Computer Vision and Pattern Recognition Computation and Language Human-Computer Interaction Multimedia |
| url | https://arxiv.org/abs/2403.17727 |