@InProceedings{ping-chen:2017:FrontiersSummarization,
  author    = {Ping, Qing  and  Chen, Chaomei},
  title     = {Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments},
  booktitle = {Proceedings of the Workshop on New Frontiers in Summarization},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1--11},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/W17-4501}
}