  Video summarization has been extensively studied in the past decades.
However, user-generated video summarization is much less explored since there
lack large-scale video datasets within which human-generated video summaries
are unambiguously defined and annotated. Toward this end, we propose a
user-generated video summarization dataset - UGSum52 - that consists of 52
videos (207 minutes). In constructing the dataset, because of the subjectivity
of user-generated video summarization, we manually annotate 25 summaries for
each video, which are in total 1300 summaries. To the best of our knowledge, it
is currently the largest dataset for user-generated video summarization.
  Based on this dataset, we present FrameRank, an unsupervised video
summarization method that employs a frame-to-frame level affinity graph to
identify coherent and informative frames to summarize a video. We use the
Kullback-Leibler(KL)-divergence-based graph to rank temporal segments according
to the amount of semantic information contained in their frames. We illustrate
the effectiveness of our method by applying it to three datasets SumMe, TVSum
and UGSum52 and show it achieves state-of-the-art results.
