  Summarizing video content is important for video streaming services to engage
the user in a limited time span. To this end, current methods involve manual
curation or using passive interest cues to annotate potential high-interest
segments to form the basis of summarized videos, and are costly and unreliable.
We propose a viewership-driven, automated method that accommodates a range of
segment identification goals. Using satellite television viewership data as a
source of ground truth for viewer interest, we apply statistical anomaly
detection on a timeline of viewership metrics to identify 'seed' segments of
high viewer interest. These segments are post-processed using empirical rules
and several sources of content metadata, e.g. shot boundaries, adding in
personalization aspects to produce the final highlights video.
  To demonstrate the flexibility of our approach, we present two case studies,
on the United States Democratic Presidential Debate on 19th December 2019, and
Wimbledon Women's Final 2019. We perform qualitative comparisons with their
publicly available highlights, as well as early vs. late viewership comparisons
for insights into possible media and social influence on viewing behavior.
