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.

本文提出了一个基于概念映射词汇链，结合评论强度和情感及概念浓度的视频焦点检测框架，通过改进的SumBasic算法概括每个检测到的焦点，实验结果表明该方法在大规模真实数据集上优于其他基准方法。

基于众源时间同步评论的情感概念映射和滞后校准的视频亮点检测和摘要