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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W17-4501.pdf