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A comprehensive comparative evaluation of RST-based summarization methods

A comprehensive comparative evaluation of RST-based summarization methods Motivated by governmental, commercial and academic interests, and due to the growing amount of information, mainly online, automatic text summarization area has experienced an increasing number of researches and products, which led to a countless number of summarization methods. In this paper, we present a comprehensive comparative evaluation of the main automatic text summarization methods based on Rhetorical Structure Theory (RST), claimed to be among the best ones. We compare our results to superficial summarizers, which belong to a paradigm with severe limitations, and to hybrid methods, combining RST and superficial methods. We also test voting systems and machine learning techniques trained on RST features. We run experiments for English and Brazilian Portuguese languages and compare the results obtained by using manually and automatically parsed texts. Our results systematically show that all RST methods have comparable overall performance and that they outperform most of the superficial methods. Machine learning techniques achieved high accuracy in the classification of text segments worth of being in the summary, but were not able to produce more informative summaries than the regular RST methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

A comprehensive comparative evaluation of RST-based summarization methods

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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Discourse
ISSN
1550-4875
DOI
10.1145/1767756.1767757
Publisher site
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Abstract

Motivated by governmental, commercial and academic interests, and due to the growing amount of information, mainly online, automatic text summarization area has experienced an increasing number of researches and products, which led to a countless number of summarization methods. In this paper, we present a comprehensive comparative evaluation of the main automatic text summarization methods based on Rhetorical Structure Theory (RST), claimed to be among the best ones. We compare our results to superficial summarizers, which belong to a paradigm with severe limitations, and to hybrid methods, combining RST and superficial methods. We also test voting systems and machine learning techniques trained on RST features. We run experiments for English and Brazilian Portuguese languages and compare the results obtained by using manually and automatically parsed texts. Our results systematically show that all RST methods have comparable overall performance and that they outperform most of the superficial methods. Machine learning techniques achieved high accuracy in the classification of text segments worth of being in the summary, but were not able to produce more informative summaries than the regular RST methods.

Journal

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

Published: May 1, 2010

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