  In this paper, we build a general summarization framework for both of edited
video and raw video summarization. Overall, our work can be divided into three
folds: 1) Four models are designed to capture the properties of video
summaries, i.e., containing important people and objects (importance),
representative to the video content (representativeness), no similar key-shots
(diversity) and smoothness of the storyline (storyness). Specifically, these
models are applicable to both edited videos and raw videos. 2) A comprehensive
score function is built with the weighted combination of the aforementioned
four models. Note that the weights of the four models in the score function,
denoted as property-weight, are learned in a supervised manner. Besides, the
property-weights are learned for edited videos and raw videos, respectively. 3)
The training set is constructed with both edited videos and raw videos in order
to make up the lack of training data. Particularly, each training video is
equipped with a pair of mixing-coefficients which can reduce the structure mess
in the training set caused by the rough mixture. We test our framework on three
datasets, including edited videos, short raw videos and long raw videos.
Experimental results have verified the effectiveness of the proposed framework.
