Live Stream Highlight Detection Using Chat Messages
Chieh-Ming Liaw, Bi-Ru Dai · 2020
In recent years, live-streaming services have been booming and are still continuing to grow on the Internet. Differing from TV shows and movies, live-streaming can have variable and longer lengths with no specific content restrictions. Traditional methods of video highlight detection, which are based on visual features, will suffer the difficulties of data scale and inconsistency. To address these issues, we alternatively extract information from the audience discussion in a chat room for high-light detection. In this paper, an attention-based model, LSTA, is proposed to integrate the long term and short term information in a chat room to determine which fragments should be identified as highlights. Our results demonstrate the improvement over both state-of-the-art visual and textual content-based approaches.