  Video summarization is a crucial research area that aims to efficiently
browse and retrieve relevant information from the vast amount of video content
available today. With the exponential growth of multimedia data, the ability to
extract meaningful representations from videos has become essential. Video
summarization techniques automatically generate concise summaries by selecting
keyframes, shots, or segments that capture the video's essence. This process
improves the efficiency and accuracy of various applications, including video
surveillance, education, entertainment, and social media. Despite the
importance of video summarization, there is a lack of diverse and
representative datasets, hindering comprehensive evaluation and benchmarking of
algorithms. Existing evaluation metrics also fail to fully capture the
complexities of video summarization, limiting accurate algorithm assessment and
hindering the field's progress. To overcome data scarcity challenges and
improve evaluation, we propose an unsupervised approach that leverages video
data structure and information for generating informative summaries. By moving
away from fixed annotations, our framework can produce representative summaries
effectively. Moreover, we introduce an innovative evaluation pipeline tailored
specifically for video summarization. Human participants are involved in the
evaluation, comparing our generated summaries to ground truth summaries and
assessing their informativeness. This human-centric approach provides valuable
insights into the effectiveness of our proposed techniques. Experimental
results demonstrate that our training-free framework outperforms existing
unsupervised approaches and achieves competitive results compared to
state-of-the-art supervised methods.
