A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
The Journal of Machine Learning Research
Text classification and named entities for new event detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Sentence Fusion for Multidocument News Summarization
Computational Linguistics
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
EnBlogue: emergent topic detection in web 2.0 streams
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Terms of a feather: content-based news recommendation and discovery using twitter
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
From chatter to headlines: harnessing the real-time web for personalized news recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
LDA-Based topic modeling in labeling blog posts with wikipedia entries
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
See what's enBlogue: real-time emergent topic identification in social media
Proceedings of the 15th International Conference on Extending Database Technology
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Due to the unprecedented popularity of social network services (SNSs), such as Twitter and Facebook, means that a huge number of user documents are created and shared constantly via SNSs. Given the volume of user documents, browsing documents in a selective manner based on personal interests is a time-consuming and laborious task. Therefore, in the case of Twitter, trend keyword lists are provided for the user's convenience. However, it is still not easy to determine the details based on a few simple keywords. The keywords usually relate to the hot issues at any time so many documents will contain pertinent details, such as news on the Internet. Thus, to provide detailed information about an issue, it is necessary to identify relationships among them. In this study, we developed a SNS-based issue detection and related news summarization scheme. To evaluate the effectiveness of our scheme, we implemented a prototype system and performed various experiments. We present some of the results.