Number of found records: 30
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LI, Hang; YAMANISHI, Kenji |
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Topic Analysis Using a Finite Mixture Model |
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Information Processing and Management, 2003, vol.39, n. 4, pp521-541 |
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Presents a single framework for conducting topic analysis that performs both topic identification and text segmentation. Key characteristics of the framework are: representing a topic by means of a cluster of words closely related to the topic; and employing a stochastic model, called a finite mixture model, to represent a word distribution within a text. (DB) |
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Information Processing; Information Retrieval; Statistical Analysis; Statistical Distributions |
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MARCU, Daniel |
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From discourse structures to text summaries |
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Proceedings of the 14th National Conference on Artificial Intelligence (AAAI-97). |
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We describe experiments that show that the concepts of rhetorical analysts and nuclearity can be used effectively for determining the most important units in a text. We show how these concepts can be implemented and we discuss results that we obtained with a discourse-based summarization program (AU) |
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rhetorical analysis; nuclearity; discourse-based summarization program |
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MATSUMOTO, Yuji; NOMOTO, Tadashi |
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The diversity-based approach to open-domain text summarization. |
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Information Processing and Management, 2003, vol. 39, n. 3, pp.363-389. |
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On line (12/05/2005) |
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AB: Introduces a novel approach to unsupervised text summarization, which in principle should work for any domain or genre. Exploits the diversity of concepts in text for summarization, which has not received much attention in the summarization literature. Proposes the information-centric approach to evaluation, where the quality of summaries is judged not in terms of how well they match human-created summaries but in terms of how well they represent their source documents in information retrieval tasks such document retrieval and text categorization. Examines how a system with the diversity functionality performs against one without, using the test data know as BMIR-J2. Demonstrates a clear superiority of the diversity-based approach to a non-diversity-based approach. Addresses how the diversity approach models human judgements on summarisation. Creates a large volume of data annotated for relevance to summarisation by human subjects. Trains a decision tree-based summarizer using the data and examines how the diversity method compares with the supervised method in performance when tested on the data. The diversity approach performs as well as, and in some cases superior to, the supervised method. (AU) |
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Electronic Text; Information Retrieval; Relevance Information Retrieval; Cluster Analysis; Cluster Grouping; Evaluation Methods; Information Seeking; Information Sources; Information Systems |
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Metatorial services inc. |
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The Difference Between Document and Content Management |
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On line (12/05/2005) |
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This presents the differences between a document and content management
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document; content management
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