Live video commenting is popular on video media platforms, as it can create a chatting atmosphere and provide supplementary information for users while watching videos. Automatically generating live video comments can improve user experience and enable human-like generation for bot chatting. Existing works mostly focus on short video datasets while ignoring other important video types such as long videos like movies. In this work, we collect a new Movie Live Comments (MovieLC) dataset to support research on live video comment generation for long videos. We also propose a knowledge enhanced generation model inspired by the divergent and informative nature of live video comments. Our model adopts a pre-training encoder-decoder framework and incorporates external knowledge. Extensive experiments show that both objective metrics and human evaluation demonstrate the effectiveness of our proposed model. The MovieLC dataset and our code will be released.

翻译：直播视频评论在视频媒体平台上广受欢迎，因为它能营造聊天氛围，并在用户观看视频时提供补充信息。自动生成直播视频评论可提升用户体验，并实现机器人聊天的类人化生成。现有研究主要关注短视频数据集，而忽略了电影等长视频等其他重要视频类型。本研究收集了一个全新的电影直播评论数据集（MovieLC），以支持长视频直播评论生成的研究。同时，受直播视频评论发散性与信息性特点的启发，我们提出了一种知识增强的生成模型。该模型采用预训练编码器-解码器框架，并融合了外部知识。大量实验表明，客观指标与人工评估均验证了所提模型的有效性。MovieLC数据集及代码将公开发布。