  In the light of exponentially increasing video content, video summarization
has attracted a lot of attention recently due to its ability to optimize time
and storage. Characteristics of a good summary of a video depend on the
particular domain under question. We propose a novel framework for domain
specific video summarization. Given a video of a particular domain, our system
can produce a summary based on what is important for that domain in addition to
possessing other desired characteristics like representativeness, coverage,
diversity etc. as suitable to that domain. Past related work has focused either
on using supervised approaches for ranking the snippets to produce summary or
on using unsupervised approaches of generating the summary as a subset of
snippets with the above characteristics. We look at the joint problem of
learning domain specific importance of segments as well as the desired summary
characteristic for that domain. Our studies show that the more efficient way of
incorporating domain specific relevances into a summary is by obtaining ratings
of shots as opposed to binary inclusion/exclusion information. We also argue
that ratings can be seen as unified representation of all possible ground truth
summaries of a video, taking us one step closer in dealing with challenges
associated with multiple ground truth summaries of a video. We also propose a
novel evaluation measure which is more naturally suited in assessing the
quality of video summary for the task at hand than F1 like measures. It
leverages the ratings information and is richer in appropriately modeling
desirable and undesirable characteristics of a summary. Lastly, we release a
gold standard dataset for furthering research in domain specific video
summarization, which to our knowledge is the first dataset with long videos
across several domains with rating annotations.
