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Video summarization aims to extract keyframes/shots from a long video.Previous methods mainly take diversity and representativeness of generatedsummaries as prior knowledge in algorithm design. In this paper, we formulatevideo summarization as a content-based recommender problem, which shoulddistill the most useful content from a long video for users who suffer frominformation overload. A scalable deep neural network is proposed on predictingif one video segment is a useful segment for users by explicitly modelling bothsegment and video. Moreover, we accomplish scene and action recognition inuntrimmed videos in order to find more correlations among different aspects ofvideo understanding tasks. Also, our paper will discuss the effect of audio andvisual features in summarization task. We also extend our work by dataaugmentation and multi-task learning for preventing the model from early-stageoverfitting. The final results of our model win the first place in ICCV 2019CoView Workshop Challenge Track.

Comprehensive Video Understanding: Video summarization with content-based video recommender design