Video summarization
videos span ten diverse categories, such as sports, news, and movies, with five videos per category. The dataset also provides frame-level and keyframe annotations derived from summaries generated by multiple volunteers.
The CoSum dataset [35] contains videos sourced from news reports and documentaries, providing sentence-based summary annotations. 34 This dataset is primarily designed for video understanding research and studies focusing on natural language summarization. The MPII Movie Description Dataset comprises video clips from various film genres, accompanied by detailed natural language descriptions. It is suitable for research on movie video summarization incorporating semantic information. The YTB dataset includes diverse videos downloaded from YouTube—spanning categories such as cartoons, news, sports, advertisements, TV programs, and home videos—with durations ranging from 1 to 10 minutes. 35 3.2 Evaluation metrics Evaluation methods for video summarization are generally divided into two categories: objective evaluation and subjective evaluation. These methods help measure the similarity between the generated summary and the original video, as well as the quality of the summary from the user’s perspective. Objective evaluation methods: objective evaluation methods for video summarization aim to assess the quality of generated summaries using quantitative metrics and algorithms. These methods rely on features of the video content, such as the diversity of keyframes, information coverage, temporal distribution, as well as factors related to the summary itself, including length and coherence. Through objective evaluation, researchers and developers can rapidly assess the performance of their summarization algorithms and conduct optimization and comparative analysis. Below are three commonly used objective evaluation metrics in the field of video summarization:
• Precision (P): measures the proportion of truly useful information within the algorithm- generated summary, i.e., the ratio of correctly predicted keyframes to all predicted segments.
34 Chu, W. et al. (2015) ‘Video co-summarization: video summarization by visual co-occurrence,’ IEEE
Conference on Computer Vision and Pattern Recognition , 2015, pp. 3584–3592 (available at
https://www.researchgate.net/publication/280298295_Video_Co-
summarization_Video_Summarization_by_Visual_Co-occurrence). 35 Gj, M. et al. (2008) ‘Comparison of automatic shot boundary detection algorithms based on color, edges and
wavelets,’ International Multiconference Information Society , 2008 (available at
https://www.researchgate.net/publication/232630485_Comparison_of_Automatic_Shot_Boundary_Detectio
n_Algorithms_Based_On_Color_Edges_and_Wavelets).
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