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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
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SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
CL research's knowledge management system
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Information Processing and Management: an International Journal
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ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
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OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems: Part II
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ER'10 Proceedings of the 2010 international conference on Advances in conceptual modeling: applications and challenges
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FG'09 Proceedings of the 14th international conference on Formal grammar
Artificial Intelligence and Law
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IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
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Information Sciences: an International Journal
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Expert Systems with Applications: An International Journal
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IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
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MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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Natural Language Engineering
Bayesian Constituent Context Model for Grammar Induction
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Coherent texts are not just simple sequences of clauses and sentences, but rather complex artifacts that have highly elaborate rhetorical structure. This paper explores the extent to which well-formed rhetorical structures can be automatically derived by means of surface-form-based algorithms. These algorithms identify discourse usages of cue phrases and break sentences into clauses, hypothesize rhetorical relations that hold among textual units, and produce valid rhetorical structure trees for unrestricted natural language texts. The algorithms are empirically grounded in a corpus analysis of cue phrases and rely on a first-order formalization of rhetorical structure trees.The algorithms are evaluated both intrinsically and extrinsically. The intrinsic evaluation assesses the resemblance between automatically and manually constructed rhetorical structure trees. The extrinsic evaluation shows that automatically derived rhetorical structures can be successfully exploited in the context of text summarization.