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Numerical and spatiotemporal features fusion for video summarization.

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Title: Numerical and spatiotemporal features fusion for video summarization.
Authors: Aboelenien, Mohamed1 (AUTHOR) mohamed.aboelenien@alumni2018.guc.edu.eg, Salem, Mohammed A.-M.1,2 (AUTHOR) mohammed.salem@guc.edu.eg
Source: Multimedia Tools & Applications. Oct2025, Vol. 84 Issue 34, p43175-43190. 16p.
Subjects: Video summarization, Deep learning
Abstract: In this paper, we propose a numerical spatiotemporal approach for video summarization. The current solutions leverage deep learning techniques to tackle this task. However, existing methods do not employ the video shots' length data in their networks. We first introduce a new ground truth labelling for video summarization. This ground truth tackles multiple users' annotations and is inclusive of video shot length information. We then propose a novel network architecture Numerical SpatioTemporal Fusion Net, NSTFNet. The proposed architecture leverages the temporal modelling ability of the self-attention mechanism to model video's vision data to be fused with structured numerical features of video shots. We evaluate our results on TVSum and SumMe datasets. Experimental results show that the proposed model outperforms state-of-the-art performance on the SumMe dataset with 51.29% and 54.42% f-scores on the canonical and augmented configurations, demonstrating the effectiveness of our proposed model compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Numerical and spatiotemporal features fusion for video summarization.
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  Data: <searchLink fieldCode="AR" term="%22Aboelenien%2C+Mohamed%22">Aboelenien, Mohamed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mohamed.aboelenien@alumni2018.guc.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Salem%2C+Mohammed+A%2E-M%2E%22">Salem, Mohammed A.-M.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> mohammed.salem@guc.edu.eg</i>
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  Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Oct2025, Vol. 84 Issue 34, p43175-43190. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Video+summarization%22">Video summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: In this paper, we propose a numerical spatiotemporal approach for video summarization. The current solutions leverage deep learning techniques to tackle this task. However, existing methods do not employ the video shots' length data in their networks. We first introduce a new ground truth labelling for video summarization. This ground truth tackles multiple users' annotations and is inclusive of video shot length information. We then propose a novel network architecture Numerical SpatioTemporal Fusion Net, NSTFNet. The proposed architecture leverages the temporal modelling ability of the self-attention mechanism to model video's vision data to be fused with structured numerical features of video shots. We evaluate our results on TVSum and SumMe datasets. Experimental results show that the proposed model outperforms state-of-the-art performance on the SumMe dataset with 51.29% and 54.42% f-scores on the canonical and augmented configurations, demonstrating the effectiveness of our proposed model compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-024-20527-x
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              Text: Oct2025
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