This paper presents a video summarization technique for an Internet video to
provide a quick way to overview its content. This is a challenging problem
because finding important or informative parts of the original video requires
to understand its content. Furthermore the content of Internet videos is very
diverse, ranging from home videos to documentaries, which makes video
summarization much more tough as prior knowledge is almost not available. To
tackle this problem, we propose to use deep video features that can encode
various levels of content semantics, including objects, actions, and scenes,
improving the efficiency of standard video summarization techniques. For this,
we design a deep neural network that maps videos as well as descriptions to a
common semantic space and jointly trained it with associated pairs of videos
and descriptions. To generate a video summary, we extract the deep features
from each segment of the original video and apply a clustering-based
summarization technique to them. We evaluate our video summaries using the
SumMe dataset as well as baseline approaches. The results demonstrated the
advantages of incorporating our deep semantic features in a video summarization
technique.

该论文提出了一种基于深度视频特征和聚类的视频摘要技术，旨在更高效地生成互联网视频的概览，该技术通过设计一种映射视频和描述的深度神经网络来将视频元素转化为语义空间。