Description: Video summarization using deep learning focuses on automatically generating short, meaningful summaries from long videos while preserving important events and context. Deep learning models, such as convolutional and recurrent neural networks, analyze visual features, motion patterns, and temporal dependencies to identify key frames and segments. Attention mechanisms and transformer-based architectures further enhance accuracy by capturing long-range relationships in video data. This technology is widely used in surveillance, content creation, education, and media management, helping reduce processing time and improve user experience. Deep learning–based summarization makes video analysis faster, smarter, and more efficient for modern applications.