A comprehensive comparative evaluation of RST-based summarization methods
ACM Transactions on Speech and Language Processing (TSLP)
Discourse indicators for content selection in summarization
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Efficient keyword extraction for meaningful document perception
Proceedings of the 11th ACM symposium on Document engineering
Computer Speech and Language
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Motivated by governmental, commercial and academic interests, 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 also propose new methods and compare our results to an extractive summarizer, which belongs to a summarization paradigm with severe limitations. To the best of our knowledge, most of our results are new in the area and reveal very interesting conclusions. The simplest RST-based method is among the best ones, although all of them present comparable results. We show that all RST-based methods overcome the extractive summarizer and that hybrid methods produce worse summaries. Finally, we verify that Mann and Thompson strong assumption in summarization and RST research area is not helpful in the way previously imagined.