  With the explosive growth of video data, video summarization, which attempts
to seek the minimum subset of frames while still conveying the main story, has
become one of the hottest topics. Nowadays, substantial achievements have been
made by supervised learning techniques, especially after the emergence of deep
learning. However, it is extremely expensive and difficult to collect human
annotation for large-scale video datasets. To address this problem, we propose
a convolutional attentive adversarial network (CAAN), whose key idea is to
build a deep summarizer in an unsupervised way. Upon the generative adversarial
network, our overall framework consists of a generator and a discriminator. The
former predicts importance scores for all frames of a video while the latter
tries to distinguish the score-weighted frame features from original frame
features. Specifically, the generator employs a fully convolutional sequence
network to extract global representation of a video, and an attention-based
network to output normalized importance scores. To learn the parameters, our
objective function is composed of three loss functions, which can guide the
frame-level importance score prediction collaboratively. To validate this
proposed method, we have conducted extensive experiments on two public
benchmarks SumMe and TVSum. The results show the superiority of our proposed
method against other state-of-the-art unsupervised approaches. Our method even
outperforms some published supervised approaches.
