  Video summarization is a technique to create a short skim of the original
video while preserving the main stories/content. There exists a substantial
interest in automatizing this process due to the rapid growth of the available
material. The recent progress has been facilitated by public benchmark
datasets, which enable easy and fair comparison of methods. Currently the
established evaluation protocol is to compare the generated summary with
respect to a set of reference summaries provided by the dataset. In this paper,
we will provide in-depth assessment of this pipeline using two popular
benchmark datasets. Surprisingly, we observe that randomly generated summaries
achieve comparable or better performance to the state-of-the-art. In some
cases, the random summaries outperform even the human generated summaries in
leave-one-out experiments. Moreover, it turns out that the video segmentation,
which is often considered as a fixed pre-processing method, has the most
significant impact on the performance measure. Based on our observations, we
propose alternative approaches for assessing the importance scores as well as
an intuitive visualization of correlation between the estimated scoring and
human annotations.
