Information retrieval using a singular value decomposition model of latent semantic structure
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search by showing results in context
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
Findex: search result categories help users when document ranking fails
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning to identify new information
Learning to identify new information
Novelty detection: the TREC experience
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
New event detection based on indexing-tree and named entity
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
'Show me more': Incremental length summarisation using novelty detection
Information Processing and Management: an International Journal
Machine-Generated Multimedia Content
ACHI '09 Proceedings of the 2009 Second International Conferences on Advances in Computer-Human Interactions
Term ranking and categorization for ad-hoc navigation
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Exploring the corporate ecosystem with a semi-supervised entity graph
Proceedings of the 20th ACM international conference on Information and knowledge management
PersonalWeb: an extensible framework to recommend web and personal information
Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation
A Computational Framework for Media Bias Mitigation
ACM Transactions on Interactive Intelligent Systems (TiiS)
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The Web makes it possible for news readers to learn more about virtually any story that interests them. Media outlets and search engines typically augment their information with links to similar stories. It is up to the user to determine what new information is added by them, if any. In this paper we present Tell Me More, a system that performs this task automatically: given a seed news story, it mines the web for similar stories reported by different sources and selects snippets of text from those stories which offer new information beyond the seed story. New content may be classified as supplying: additional quotes, additional actors, additional figures and additional information depending on the criteria used to select it. In this paper we describe how the system identifies new and informative content with respect to a news story. We also how that providing an explicit categorization of new information is more useful than a binary classification (new/not-new). Lastly, we show encouraging results from a preliminary evaluation of the system that validates our approach and encourages further study.