  Human communication takes many forms, including speech, text and
instructional videos. It typically has an underlying structure, with a starting
point, ending, and certain objective steps between them. In this paper, we
consider instructional videos where there are tens of millions of them on the
Internet.
  We propose a method for parsing a video into such semantic steps in an
unsupervised way. Our 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. Our method can also provide a
textual description for each of the identified semantic steps and video
segments. We evaluate our method on a large number of complex YouTube videos
and show that our method discovers semantically correct instructions for a
variety of tasks.
