Multimedia Tools and Applications

, Volume 73, Issue 2, pp 857–872 | Cite as

TrendsSummary: a platform for retrieving and summarizing trendy multimedia contents

  • Daehoon Kim
  • Daeyong Kim
  • Sanghoon Jun
  • Seungmin Rho
  • Eenjun Hwang


With the flood and popularity of various multimedia contents on the Internet, searching for appropriate contents and representing them effectively has become an essential part for user satisfaction. So far, many contents recommendation systems have been proposed for this purpose. A popular approach is to select hot or popular contents for recommendation using some popularity metric. Recently, various social network services (SNSs) such as Facebook and Twitter have become a widespread social phenomenon owing to the smartphone boom. Considering the popularity and user participation, SNS can be a good source for finding social interests or trends. In this study, we propose a platform called TrendsSummary for retrieving trendy multimedia contents and summarizing them. To identify trendy multimedia contents, we select candidate keywords from raw data collected from Twitter using a syntactic feature-based filtering method. Then, we merge various keyword variants based on several heuristics. Next, we select trend keywords and their related keywords from the merged candidate keywords based on term frequency and expand them semantically by referencing portal sites such as Wikipedia and Google. Based on the expanded trend keywords, we collect four types of relevant multimedia contents—TV programs, videos, news articles, and images—from various websites. The most appropriate media type for the trend keywords is determined based on a naïve Bayes classifier. After classification, appropriate contents are selected from among the contents of the selected media type. Finally, both trend keywords and their related multimedia contents are displayed for effective browsing. We implemented a prototype system and experimentally demonstrated that our scheme provides satisfactory results.


Twitter Trends Multimedia contents recommendation Summarization Naïve Bayes classifier TreeMap 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2013R1A1A2012627) and the MSIP(Ministry of Science, ICT&Future Planning), Korea, under the C-ITRC(Convergence Information Technology Research Center) support program (NIPA-2013-H0301-13-3006) supervised by the NIPA(National IT Industry Promotion Agency)


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Daehoon Kim
    • 1
  • Daeyong Kim
    • 1
  • Sanghoon Jun
    • 1
  • Seungmin Rho
    • 2
  • Eenjun Hwang
    • 1
  1. 1.School of Electrical EngineeringKorea UniversitySeoulSouth Korea
  2. 2.Department of MultimediaSungkyul UniversityAnyang-siSouth Korea

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