  The assignment of importance scores to particular frames or (short) segments
in a video is crucial for summarization, but also a difficult task. Previous
work utilizes only one source of visual features. In this paper, we suggest a
novel model architecture that combines three feature sets for visual content
and motion to predict importance scores. The proposed architecture utilizes an
attention mechanism before fusing motion features and features representing the
(static) visual content, i.e., derived from an image classification model.
Comprehensive experimental evaluations are reported for two well-known
datasets, SumMe and TVSum. In this context, we identify methodological issues
on how previous work used these benchmark datasets, and present a fair
evaluation scheme with appropriate data splits that can be used in future work.
When using static and motion features with parallel attention mechanism, we
improve state-of-the-art results for SumMe, while being on par with the state
of the art for the other dataset.
