Video summarization
• Temporal Coverage (recall, R): measures the extent to which the algorithm can retrieve all the useful segments manually annotated, i.e., the proportion of predicted keyframes that overlap with the ground truth summary segments. • F-Score (F): a harmonic mean of precision and recall, providing a more comprehensive evaluation of the algorithm’s overall performance. The F-Score is calculated as follows:
2𝑃𝑅 𝑃+𝑅
Subjective evaluation involves human viewers or experts who assess the quality of generated summaries based on their personal perceptions and judgments. Common approaches include user surveys, user experience testing, and expert reviews. By observing participants’ reactions, ratings, and feedback, researchers can gain insights into the perceived quality of summaries. This type of evaluation provides intuitive and comprehensive results, helping researchers understand user preferences, likes, and satisfaction levels, thereby guiding the optimization of summarization algorithms. • User surveys: participants are presented with generated video summaries and asked to provide feedback and ratings to capture their subjective impressions of summary quality. • User viewing behavior analysis: monitoring user behaviors while watching video summaries— such as viewing duration and click counts—serves to evaluate the attractiveness and effectiveness of the summaries. • User satisfaction surveys: collecting data on user satisfaction regarding the generated summaries helps determine whether the summaries meet user expectations. 3.3 Comparison of methods and results To evaluate the overall performance of various unsupervised video summarization methods, we report their F-score results on two widely used benchmark datasets: SumMe and TVSum. The F-score, as the harmonic mean of precision and recall, provides a balanced measure of both accuracy and coverage in identifying keyframes. The selected methods cover a range of algorithmic strategies, including clustering-based approaches, adversarial learning frameworks, and reinforcement learning techniques. By comparing their F-scores across datasets, we can better understand each method's generalization ability and effectiveness in diverse video scenarios. Table 1: Comparison of F-scores of different unsupervised algorithms on two datasets Algorithm SumMe TVSum OnlineMotion-AE[20] 37.7 51.5 SUM-FCN[32] 41.5 52.7 PCDL[29] 42.7 58.4
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