TAILIEUCHUNG - Báo cáo khoa học: "a Topic-Model based approach for update summarization"

Update summarization is a new challenge in multi-document summarization focusing on summarizing a set of recent documents relatively to another set of earlier documents. We present an unsupervised probabilistic approach to model novelty in a document collection and apply it to the generation of update summaries. The new model, called D UAL S UM, results in the second or third position in terms of the ROUGE metrics when tuned for previous TAC competitions and tested on TAC-2011, being statistically indistinguishable from the winning system. A manual evaluation of the generated summaries shows state-of-the art results for D UAL S. | DualSum a Topic-Model based approach for update summarization Jean-Yves Delort Google Research Brandschenkestrasse 110 8002 Zurich Switzerland jydelort@ Enrique Alfonseca Google Research Brandschenkestrasse 110 8002 Zurich Switzerland ealfonseca@ Abstract Update summarization is a new challenge in multi-document summarization focusing on summarizing a set of recent documents relatively to another set of earlier documents. We present an unsupervised probabilistic approach to model novelty in a document collection and apply it to the generation of update summaries. The new model called DualSum results in the second or third position in terms of the ROUGE metrics when tuned for previous TAC competitions and tested on TAC-2011 being statistically indistinguishable from the winning system. A manual evaluation of the generated summaries shows state-of-the art results for DualSum with respect to focus coherence and overall responsiveness. 1 Introduction Update summarization is the problem of extracting and synthesizing novel information in a collection of documents with respect to a set of documents assumed to be known by the reader. This problem has received much attention in recent years as can be observed in the number of participants to the special track on update summarization organized by DUC and TAC since 2007. The problem is usually formalized as follows Given two collections A and B where the documents in A chronologically precede the documents in B generate a summary of B under the assumption that the user of the summary has already read the documents in A. Extractive techniques are the most common approaches in multi-document summarization. Summaries generated by such techniques consist of sentences extracted from the document collection. Extracts can have coherence and cohesion problems but they generally offer a good tradeoff between linguistic quality and informativeness. While numerous extractive summarization techniques have been .

• Báo cáo khoa học
• a Topic Model based approach
• update summarization
• Jean Yves Delort
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
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• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
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• Barbara J
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• PLANNING NATURAL LANGUAGE
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• Appelt
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• MENTING A DATABASE
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• THE KEY TO THE SELECTION PROBLEM
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• Jeffrey Conklin David D
• McDonald
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• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
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