{"title":"微博热门话题的多媒体总结","authors":"Jingwen Bian, Yang Yang, Tat-Seng Chua","doi":"10.1145/2505515.2505652","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":20528,"journal":{"name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","volume":"58 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"69","resultStr":"{\"title\":\"Multimedia summarization for trending topics in microblogs\",\"authors\":\"Jingwen Bian, Yang Yang, Tat-Seng Chua\",\"doi\":\"10.1145/2505515.2505652\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":20528,\"journal\":{\"name\":\"Proceedings of the 22nd ACM international conference on Information & Knowledge Management\",\"volume\":\"58 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"69\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 22nd ACM international conference on Information & Knowledge Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2505515.2505652\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2505515.2505652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.