HOTVCOM: Generating Buzzworthy Comments for Videos

Autor:	Chen, Yuyan, Qian, Yiwen, Yan, Songzhou, Jia, Jiyuan, Li, Zhixu, Xiao, Yanghua, Li, Xiaobo, Yang, Ming, Guo, Qingpei
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or ``danmaku'' in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces \textsc{HotVCom}, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the \texttt{ComHeat} framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets. Comment: Accepted to ACL 2024 (Findings)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.15196 Zobrazit plný text záznamu View this record from Arxiv