Multi-Modal Video Summarization Using Deep Learning and Attention Mechanisms

Efficient video summarization for tasks such as content retrieval, surveillance, and media browsing in today's ubiquitous multimedia content era is necessary. The paper presents a novel framework for multi-modal video summarization that can powerfully combine the visual, audio, and textual modalities using advanced deep learning and attention mechanisms. The proposed approach employs ResNet-50 for rich extraction of visual features, bi-directional LSTM networks for extracting temporal dependencies, and attention layers for maximizing the informative segments that can represent the findings along different modalities. Putting all of the above together ultimately generates concise and representative summaries that retain the essentials of the original videos. We validate the effectiveness of our approach on benchmark datasets, namely SumMe and TVSum, on which we compare its performance with state-of-the-art techniques for video summarization. Results show how our method significantly surpasses current methods with an F1-score as high as 0.72 in SumMe and 0.75 on TVSum, respectively, concerning 0.60 and 0.63, reported by methods such as VSUMM. Hence, these results say that the summaries from our model would be rich and informative simultaneously.