Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Exploring content models for multi-document summarization
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Summarizing microblogs automatically
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Towards Twitter context summarization with user influence models
Proceedings of the sixth ACM international conference on Web search and data mining
Effective transfer tagging from image to video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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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.