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Summarization system evaluation revisited: N-gram graphs

Summarization system evaluation revisited: N-gram graphs Summarization System Evaluation Revisited: N-Gram Graphs GEORGE GIANNAKOPOULOS and VANGELIS KARKALETSIS National Centre for Scienti c Research Demokritos GEORGE VOUROS University of the Aegean and PANAGIOTIS STAMATOPOULOS University of Athens This article presents a novel automatic method (AutoSummENG) for the evaluation of summarization systems, based on comparing the character n-gram graphs representation of the extracted summaries and a number of model summaries. The presented approach is language neutral, due to its statistical nature, and appears to hold a level of evaluation performance that matches and even exceeds other contemporary evaluation methods. Within this study, we measure the effectiveness of different representation methods, namely, word and character n-gram graph and histogram, different n-gram neighborhood indication methods as well as different comparison methods between the supplied representations. A theory for the a priori determination of the methods ™ parameters along with supporting experiments concludes the study to provide a complete alternative to existing methods concerning the automatic summary system evaluation process. Categories and Subject Descriptors: I.2.7 [Arti cial Intelligence]: Natural Language Processing ”Text analysis, language models General Terms: Algorithms, Languages, Measurement, Performance Additional Key Words and Phrases: Automatic summarization, summarization evaluation, n-gram graph The research described within this article was http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

Summarization system evaluation revisited: N-gram graphs

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2008 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/1410358.1410359
Publisher site
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Abstract

Summarization System Evaluation Revisited: N-Gram Graphs GEORGE GIANNAKOPOULOS and VANGELIS KARKALETSIS National Centre for Scienti c Research Demokritos GEORGE VOUROS University of the Aegean and PANAGIOTIS STAMATOPOULOS University of Athens This article presents a novel automatic method (AutoSummENG) for the evaluation of summarization systems, based on comparing the character n-gram graphs representation of the extracted summaries and a number of model summaries. The presented approach is language neutral, due to its statistical nature, and appears to hold a level of evaluation performance that matches and even exceeds other contemporary evaluation methods. Within this study, we measure the effectiveness of different representation methods, namely, word and character n-gram graph and histogram, different n-gram neighborhood indication methods as well as different comparison methods between the supplied representations. A theory for the a priori determination of the methods ™ parameters along with supporting experiments concludes the study to provide a complete alternative to existing methods concerning the automatic summary system evaluation process. Categories and Subject Descriptors: I.2.7 [Arti cial Intelligence]: Natural Language Processing ”Text analysis, language models General Terms: Algorithms, Languages, Measurement, Performance Additional Key Words and Phrases: Automatic summarization, summarization evaluation, n-gram graph The research described within this article was

Journal

ACM Transactions on Speech and Language Processing (TSLP) – Association for Computing Machinery

Published: Oct 1, 2008

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