  Today's popular TV series tend to develop continuous, complex plots spanning
several seasons, but are often viewed in controlled and discontinuous
conditions. Consequently, most viewers need to be re-immersed in the story
before watching a new season. Although discussions with friends and family can
help, we observe that most viewers make extensive use of summaries to re-engage
with the plot. Automatic generation of video summaries of TV series' complex
stories requires, first, modeling the dynamics of the plot and, second,
extracting relevant sequences. In this paper, we tackle plot modeling by
considering the social network of interactions between the characters involved
in the narrative: substantial, durable changes in a major character's social
environment suggest a new development relevant for the summary. Once
identified, these major stages in each character's storyline can be used as a
basis for completing the summary with related sequences. Our algorithm combines
such social network analysis with filmmaking grammar to automatically generate
character-oriented video summaries of TV series from partially annotated data.
We carry out evaluation with a user study in a real-world scenario: a large
sample of viewers were asked to rank video summaries centered on five
characters of the popular TV series Game of Thrones, a few weeks before the
new, sixth season was released. Our results reveal the ability of
character-oriented summaries to re-engage viewers in television series and
confirm the contributions of modeling the plot content and exploiting stylistic
patterns to identify salient sequences.
