Bridging Video Content and Comments: Synchronized Video Description with Temporal Summarization of Crowdsourced Time-Sync Comments

Authors

  • Linli Xu University of Science and Technology of China
  • Chao Zhang University of Science and Technology of China

DOI:

https://doi.org/10.1609/aaai.v31i1.10753

Keywords:

video description, temporal summarization, time-sync comments

Abstract

With the rapid growth of online sharing media, we are facing a huge collection of videos. In the meantime, due to the volume and complexity of video data, it can be tedious and time consuming to index or annotate videos. In this paper, we propose to generate temporal descriptions of videos by exploiting the information of crowdsourced time-sync comments which are receiving increasing popularity on many video sharing websites. In this framework, representative and interesting comments of a video are selected and highlighted along the timeline, which provide an informative description of the video in a time-sync manner. The challenge of the proposed application comes from the extremely informal and noisy nature of the comments, which are usually short sentences and on very different topics. To resolve these issues, we propose a novel temporal summarization model based on the data reconstruction principle, where representative comments are selected in order to best reconstruct the original corpus at the text level as well as the topic level while incorporating the temporal correlations of the comments. Experimental results on real-world data demonstrate the effectiveness of the proposed framework and justify the idea of exploiting crowdsourced time-sync comments as a bridge to describe videos.

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Published

2017-02-12

How to Cite

Xu, L., & Zhang, C. (2017). Bridging Video Content and Comments: Synchronized Video Description with Temporal Summarization of Crowdsourced Time-Sync Comments. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10753

Issue

Section

Main Track: Machine Learning Applications