Scaling New Peaks: A Viewership-centric Approach to Automated Content Curation

TL;DR

This paper introduces a viewership-driven, automated content curation method for video summarization that leverages viewership data and anomaly detection to identify high-interest segments, improving efficiency and personalization.

Contribution

It presents a novel automated approach using viewership data and anomaly detection for segment identification, enhancing content curation without manual intervention.

Findings

Successfully identified high-interest segments using viewership data

Demonstrated approach on political debate and sports event case studies

Provided insights into viewer behavior and content engagement

Abstract

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.