We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also des.cdbe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.

We introduce CST (cross-document structure theory), a paradigm for multi-document analysis. CST takes into account the rhetorical structure of dusters of related textual documents. We present a taxonomy of cross-document relationships. We argue that CST can be the basis for multi-document summarization guided by user preferences for summary length, information provenmace, cross-source agreement, and chronological ordering of facts.

Abstract. In this paper, an adaptive news event detection method is proposed. We consider a news event as a life form and propose an aging theory to model its life span. A news event becomes popular with a burst of news reports, and it fades away with time. We incorporate the proposed aging theory into the traditional single-pass clustering algorithm to model life spans of news events. Experiment results show that the proposed method has fairly good performance for both long-running and short-term events compared to other approaches. 1

OF THE DISSERTATION New Techniques in Intelligent Information Filtering by Sofus Attila Macskassy Dissertation Director: Dr. Haym Hirsh Intelligent Information Filtering is the process of receiving or monitoring large amounts of dynamically generated information and extracting the subset of information that would be of interest to a user based on some specified information need. Historically, this need has been based on user profiles that are directly evaluable---the information can be immediately classified as interesting or not. In this thesis I introduce a new type of user interestingness criterion which is prospective---the criterion defines the interestingness of an information item based on events that happen subsequent to the information item appearing. Hence, the interestingness cannot be directly evaluated. A new technique is described which takes such a criterion and operationalizes it, using machine learning to generate a predictive model that can directly evaluate a piece of information. I show that this technique works statistically significantly better than the baseline of predicting based on class distribution on five information filtering case studies.

Under consideration for other conferences (specify)? NO We introduce CST (cross-document structure theory), a paradigm for multi-document analysis. CST takes into account the rhetorical structure of clusters of related textual documents. We present a taxonomy of cross-document relationships. We argue that CST can be the basis for multi-document summarization guided by user preferences for summary length, information provenance, crosssource agreement, and chronological ordering of facts. ACL-411