  Human communication typically has an underlying structure. This is reflected
in the fact that in many user generated videos, a starting point, ending, and
certain objective steps between these two can be identified. In this paper, we
propose a method for parsing a video into such semantic steps in an
unsupervised way. The proposed method is capable of providing a semantic
"storyline" of the video composed of its objective steps. We accomplish this
using both visual and language cues in a joint generative model. The proposed
method can also provide a textual description for each of the identified
semantic steps and video segments. We evaluate this method on a large number of
complex YouTube videos and show results of unprecedented quality for this
intricate and impactful problem.
