Temporal summaries of new topics
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
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ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Document update summarization using incremental hierarchical clustering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Topic Pages: An Alternative to the Ten Blue Links
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Applying regression models to query-focused multi-document summarization
Information Processing and Management: an International Journal
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
Out of sight, not out of mind: on the effect of social and physical detachment on information need
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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During unexpected events such as natural disasters, individuals rely on the information generated by news outlets to form their understanding of these events. This information, while often voluminous, is frequently degraded by the inclusion of unimportant, duplicate, or wrong information. It is important to be able to present users with only the novel, important information about these events as they develop. We present the problem of updating users about time critical news events, and focus on the task of deciding which information to select for updating users as an event develops. We propose a solution to this problem which incorporates techniques from information retrieval and multi-document summarization and evaluate this approach on a set of historic events using a large stream of news documents. We also introduce an evaluation method which is significantly less expensive than traditional approaches to temporal summarization.