Real-Time Video Summarizer for LMS With Temporal Modeling and Attention Mechanism Using Deep Learning

Developments in video content across various learning platforms require new advanced techniques to efficiently manage and streamline vast video data access. Using Recurrent Neural Networks (RNNs) and Bidirectional Long Short-Term Memory (BiLSTMs) together with attention mechanisms the paper addresses real-time video summarization for learning long duration videos and courses. The developed system demonstrates functionality to detect essential keyframes while maintaining control over time-based sequence relations in video content. Video summaries created by humans serve together with raw video content to enable the model to discover vital visual data and contextual associations. Standards measuring effectiveness include BLEU and ROUGE which help assure both clear and coherent results from generated summaries. The proposed method demonstrates real-time summary generation accuracy for different video types in learning courses based on user preferences which makes it a strong practical automatic video summarization technique for the learning platform.