微博热门话题的多媒体总结

Jingwen Bian, Yang Yang, Tat-Seng Chua
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引用次数: 69

摘要

微博服务彻底改变了人们交换信息的方式。面对越来越多的多媒体内容和热门话题的微博,提供可视化的摘要帮助用户快速掌握话题的本质是很有必要的。现有的研究大多集中在基于文本的方法上,而对多种媒体类型(如文本和图像)的总结研究甚少。在本文中,我们提出了一个多媒体微博摘要框架来自动生成趋势话题的可视化摘要。具体而言,提出了一种新的生成概率模型,即多模态lda (MMLDA),通过探索不同媒体类型之间的相关性,从微博中发现子主题。基于MMLDA获取的信息,设计了一个多媒体摘要器,分别识别具有代表性的文本样本和视觉样本,形成全面的可视化摘要。我们在真实的新浪微博数据集上进行了广泛的实验,以证明我们提出的方法相对于最先进的方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimedia summarization for trending topics in microblogs
Microblogging services have revolutionized the way people exchange information. Confronted with the ever-increasing numbers of microblogs with multimedia contents and trending topics, it is desirable to provide visualized summarization to help users to quickly grasp the essence of topics. While existing works mostly focus on text-based methods only, summarization of multiple media types (e.g., text and image) are scarcely explored. In this paper, we propose a multimedia microblog summarization framework to automatically generate visualized summaries for trending topics. Specifically, a novel generative probabilistic model, termed multimodal-LDA (MMLDA), is proposed to discover subtopics from microblogs by exploring the correlations among different media types. Based on the information achieved from MMLDA, a multimedia summarizer is designed to separately identify representative textual and visual samples and then form a comprehensive visualized summary. We conduct extensive experiments on a real-world Sina Weibo microblog dataset to demonstrate the superiority of our proposed method against the state-of-the-art approaches.
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