  Video summarization aims at generating a compact yet representative visual
summary that conveys the essence of the original video. The advantage of
unsupervised approaches is that they do not require human annotations to learn
the summarization capability and generalize to a wider range of domains.
Previous work relies on the same type of deep features, typically based on a
model pre-trained on ImageNet data. Therefore, we propose the incorporation of
multiple feature sources with chunk and stride fusion to provide more
information about the visual content. For a comprehensive evaluation on the two
benchmarks TVSum and SumMe, we compare our method with four state-of-the-art
approaches. Two of these approaches were implemented by ourselves to reproduce
the reported results. Our evaluation shows that we obtain state-of-the-art
results on both datasets, while also highlighting the shortcomings of previous
work with regard to the evaluation methodology. Finally, we perform error
analysis on videos for the two benchmark datasets to summarize and spot the
factors that lead to misclassifications.
