Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays
IEEE Intelligent Systems
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The automatic translation of discourse structures
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Veins Theory: a model of global discourse cohesion and coherence
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Abstract generation based on rhetorical structure extraction
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Coherence in natural language: data structures and applications
Coherence in natural language: data structures and applications
Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evaluation of Automatic Text Summarization Methods Based on Rhetorical Structure Theory
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 02
The automatic creation of literature abstracts
IBM Journal of Research and Development
Machine-made index for technical literature: an experiment
IBM Journal of Research and Development
GistSumm: a summarization tool based on a new extractive method
PROPOR'03 Proceedings of the 6th international conference on Computational processing of the Portuguese language
Review and evaluation of dizer – an automatic discourse analyzer for brazilian portuguese
PROPOR'06 Proceedings of the 7th international conference on Computational Processing of the Portuguese Language
Revisiting centrality-as-relevance: support sets and similarity as geometric proximity
Journal of Artificial Intelligence Research
Discourse structure and language technology
Natural Language Engineering
Self reinforcement for important passage retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.