  In this paper, we address the problem of unsupervised video summarization
that automatically extracts key-shots from an input video. Specifically, we
tackle two critical issues based on our empirical observations: (i) Ineffective
feature learning due to flat distributions of output importance scores for each
frame, and (ii) training difficulty when dealing with long-length video inputs.
To alleviate the first problem, we propose a simple yet effective
regularization loss term called variance loss. The proposed variance loss
allows a network to predict output scores for each frame with high discrepancy
which enables effective feature learning and significantly improves model
performance. For the second problem, we design a novel two-stream network named
Chunk and Stride Network (CSNet) that utilizes local (chunk) and global
(stride) temporal view on the video features. Our CSNet gives better
summarization results for long-length videos compared to the existing methods.
In addition, we introduce an attention mechanism to handle the dynamic
information in videos. We demonstrate the effectiveness of the proposed methods
by conducting extensive ablation studies and show that our final model achieves
new state-of-the-art results on two benchmark datasets.
