Multi-scale temporal convolutions in a memory-augmented extended LSTM for dynamic video summarization

Capturing relevant content from videos while preserving temporal coherence remains a central challenge in video skimming, particularly in the presence of redundant and repetitive visual patterns. Conventional approaches often struggle to model temporal dependencies across different granularities, leading to suboptimal summaries that fail to balance local consistency and global structure. In this work, we propose the MU lti- S ca L e M emory- A ugmented L STM for Dynamic Video Summ arization ( MUSL-MALSumm ), a supervised architecture based on Extended Long Short-Term Memory (xLSTM), which introduces a multi-scale temporal modeling strategy to better capture both short- and long-range dependencies. Specifically, the proposed method integrates parallel temporal convolutions with different receptive fields to encode multi-scale patterns, combined with a residual projection mechanism that preserves locally informative features while enabling effective global temporal reasoning through recurrent dynamics. This design enhances the model’s ability to maintain temporal coherence and robustness to noisy or redundant frames, while preserving a low computational footprint. Experimental results demonstrate the effectiveness of the proposed approach, achieving an average F-score of 53.92 on the SumMe dataset and 62.38 on TVSum, outperforming recent supervised baselines. Additionally, the model attains a Kendall’s τ of 0.184 and Spearman’s ρ of 0.246, indicating strong alignment with human annotations. These findings highlight the benefits of multi-scale temporal modeling in video summarization, providing a competitive and efficient solution for generating coherent and informative video summaries.