Abstract Multi-video summarization is an effective tool for users to browse multiple videos. In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection. Two strategies are proposed to solve the inherent class imbalance problem of video summarization task. Moreover, we propose a diversity regularization to encourage the model to generate a diverse summary. Extensive experiments are conducted, and the comparisons are carried out with the state-of-the-art video summarization methods, the traditional and novel graph models. Our method achieves state-of-the-art performances on two standard video summarization datasets. The results demonstrate the effectiveness of our proposed model in generating a representative summary for multiple videos with good diversity.

中文翻译：

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用于多视频摘要的动态图卷积网络

摘要 多视频摘要是用户浏览多个视频的有效工具。在本文中，多视频摘要被表述为图分析问题，并提出了动态图卷积网络来衡量每个视频镜头在其自己的视频以及整个视频集合中的重要性和相关性。提出了两种策略来解决视频摘要任务固有的类不平衡问题。此外，我们提出了多样性正则化以鼓励模型生成多样化的摘要。进行了广泛的实验，并与最先进的视频摘要方法、传统和新颖的图模型进行了比较。我们的方法在两个标准视频摘要数据集上实现了最先进的性能。