  Video summarization aims to extract keyframes/shots from a long video.
Previous methods mainly take diversity and representativeness of generated
summaries as prior knowledge in algorithm design. In this paper, we formulate
video summarization as a content-based recommender problem, which should
distill the most useful content from a long video for users who suffer from
information overload. A scalable deep neural network is proposed on predicting
if one video segment is a useful segment for users by explicitly modelling both
segment and video. Moreover, we accomplish scene and action recognition in
untrimmed videos in order to find more correlations among different aspects of
video understanding tasks. Also, our paper will discuss the effect of audio and
visual features in summarization task. We also extend our work by data
augmentation and multi-task learning for preventing the model from early-stage
overfitting. The final results of our model win the first place in ICCV 2019
CoView Workshop Challenge Track.
