  The massive growth of sports videos has resulted in a need for automatic
generation of sports highlights that are comparable in quality to the
hand-edited highlights produced by broadcasters such as ESPN. Unlike previous
works that mostly use audio-visual cues derived from the video, we propose an
approach that additionally leverages contextual cues derived from the
environment that the game is being played in. The contextual cues provide
information about the excitement levels in the game, which can be ranked and
selected to automatically produce high-quality basketball highlights. We
introduce a new dataset of 25 NCAA games along with their play-by-play stats
and the ground-truth excitement data for each basket. We explore the
informativeness of five different cues derived from the video and from the
environment through user studies. Our experiments show that for our study
participants, the highlights produced by our system are comparable to the ones
produced by ESPN for the same games.
