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Explainable Video Summarization for Advancing Media Content Production
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Author(s): Evlampios Apostolidis (Information Technologies Institute, Centre for Research and Technology, Hellas, Greece), Georgios Balaouras (Information Technologies Institute, Centre for Research and Technology, Hellas, Greece), Ioannis Patras (School of Electronic Engineering and Computer Science, Queen Mary University of London, UK)and Vasileios Mezaris (Information Technologies Institute, Centre for Research and Technology, Hellas, Greece)
Copyright: 2025
Pages: 24
Source title:
Encyclopedia of Information Science and Technology, Sixth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/978-1-6684-7366-5.ch065
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Abstract
This chapter focuses on explainable video summarization, a technology that could significantly advance the content production workflow of Media organizations. It starts by presenting the current state of the art in the fields of deep-learning-based video summarization and explainable video analysis and understanding. Following, it focuses on video summarization methods that rely on the use of attention mechanisms and reports on previous works that investigated the use of attention for explaining the outcomes of deep neural networks. Subsequently, it briefly describes a state-of-the-art attention-based architecture for unsupervised video summarization and discusses a recent work that examines the use of various attention-based signals for explaining the outcomes of video summarization. Finally, it provides recommendations about future research directions.
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