  With the prevalence of video sharing, there are increasing demands for
automatic video digestion such as highlight detection. Recently, platforms with
crowdsourced time-sync video comments have emerged worldwide, providing a good
opportunity for highlight detection. However, this task is non-trivial: (1)
time-sync comments often lag behind their corresponding shot; (2) time-sync
comments are semantically sparse and noisy; (3) to determine which shots are
highlights is highly subjective. The present paper aims to tackle these
challenges by proposing a framework that (1) uses concept-mapped lexical-chains
for lag calibration; (2) models video highlights based on comment intensity and
combination of emotion and concept concentration of each shot; (3) summarize
each detected highlight using improved SumBasic with emotion and concept
mapping. Experiments on large real-world datasets show that our highlight
detection method and summarization method both outperform other benchmarks with
considerable margins.
