In this paper, we address the problem of video summarization, which aims at selecting a subset of video frames as a summary to represent the original video contents compactly and completely. We propose a simple but effective supervised approach with a dual attention capsule network towards this end. Unlike existing LSTM based methods, it pays attention to short- and long-term dependencies among video frames through an elaborate dual self-attention architecture, which can handle longer-term dependencies and admit parallel computing. To reconcile the outputs of dual self-attention, we rely on a two-stream capsule network to learn the underlying frame selection criteria. Experiments on real-world datasets show the advantages of the proposed approach compared with state-of-the-art methods.