  Live streaming platforms need to store a lot of recorded live videos on a
daily basis. An important problem is how to automatically extract highlights
(i.e., attractive short video clips) from these massive, long recorded live
videos. One approach is to directly apply a highlight extraction algorithm to
video content. However, algorithmic approaches are either domain-specific,
which require experts to spend a long time to design, or resource-intensive,
which require a lot of training data and/or computing resources. In this paper,
we propose Lightor, a novel implicit crowdsourcing approach to overcome these
limitations. The key insight is to collect users' natural interactions with a
live streaming platform, and then leverage them to detect highlights. Lightor
consists of two major components. Highlight Initializer collects time-stamped
chat messages from a live video and then uses them to predict approximate
highlight positions. Highlight Extractor keeps track of how users interact with
these approximate highlight positions and then refines these positions
iteratively. We find that the collected user chat and interaction data are very
noisy, and propose effective techniques to deal with noise. Lightor can be
easily deployed into existing live streaming platforms, or be implemented as a
web browser extension. We recruit hundreds of users from Amazon Mechanical
Turk, and evaluate the performance of Lightor using two popular games in
Twitch. The results show that Lightor can achieve high extraction precision
with a small set of training data and low computing resources.
