Video Skimming Methods for Making Short Informative Videos

The exponential growth of video content across domains such as entertainment, surveillance, and personal life-logging has intensified the demand for efficient video summarization techniques. Traditional summarization approaches often lack semantic understanding and personalization, resulting in generic outputs that fail to meet diverse user preferences. This work reviews state-of-the-art deep learning methods for personalized video summarization, emphasizing their mathematical foundations, performance metrics, and optimization strategies. Key models include reinforcement learning frameworks balancing coverage, diversity, and redundancy; semantic-driven approaches leveraging feature detectors; graph-based and sketch-driven methods for interactive customization; and emotion or gazebased systems enhancing user engagement. Comparative evaluation reveals that semanticenriched and reinforcement learning approaches achieve superior accuracy, scalability, and storage efficiency. The study concludes that future directions lie in multimodal fusion, lightweight architectures, and explainable AI to ensure adaptability, efficiency, and trust. These findings advance the development of user-centric, scalable video summarization systems.