MGraph: multimodal event summarization in social media using topic models and graph-based ranking

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• 作者：Manos Schinas ; Symeon Papadopoulos…
• 关键词：Event summarization ; Social media ; Multimedia ranking ; Diverse image retrieval
• 刊名：International Journal of Multimedia Information Retrieval
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：5
• 期：1
• 页码：51-69
• 全文大小：2,395 KB
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• 作者单位：Manos Schinas (1)
  Symeon Papadopoulos (1)
  Yiannis Kompatsiaris (1)
  Pericles A. Mitkas (2)
  
  1. Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), 57001, Thessaloniki, Greece
  2. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
  
• 刊物主题：Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl. Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general;
• 出版者：Springer London
• ISSN：2192-662X

文摘

Due to the increasing popularity of social media platforms, the amount of messages (posts) related to public events, especially posts sharing multimedia content, is steadily increasing. Sharing images can contribute to a rich and live coverage of the event. Yet, despite the value and interestingness of some posts, there is a lot of spam and redundancy, which makes it challenging to select the most important and characteristic posts for the event. In this work, we describe MGraph, a summarization framework that, given a set of social media posts about an event, selects a subset of shared images, simultaneously maximizing their relevance and minimizing their visual redundancy. MGraph employs a topic modelling technique based on different modalities to capture the relevance of posts to event topics, and a graph-based ranking algorithm to produce a diverse ranking of the selected high-relevance images. A user-centred evaluation on a dataset comprising a variety of real-world events demonstrates that MGraph considerably outperforms a number of state-of-the-art summarization algorithms in terms of relevance and diversity (25 and 7 % improvement respectively). Keywords Event summarization Social media Multimedia ranking Diverse image retrieval