Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments

Qing Ping, Chaomei Chen


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.
Anthology ID:
W17-4501
Volume:
Proceedings of the Workshop on New Frontiers in Summarization
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Lu Wang, Jackie Chi Kit Cheung, Giuseppe Carenini, Fei Liu
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://aclanthology.org/W17-4501
DOI:
10.18653/v1/W17-4501
Bibkey:
Cite (ACL):
Qing Ping and Chaomei Chen. 2017. Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments. In Proceedings of the Workshop on New Frontiers in Summarization, pages 1–11, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments (Ping & Chen, 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-4501.pdf