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
Statistics-Based Summarization - Step One: Sentence Compression
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Sentence reduction for automatic text summarization
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Forest-based statistical sentence generation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Motivations and methods for text simplification
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Deep Read: a reading comprehension system
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Query-relevant summarization using FAQs
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Headline generation based on statistical translation
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
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
Induction of Word and Phrase Alignments for Automatic Document Summarization
Computational Linguistics
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
Statistical Model for Japanese Abbreviations
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Search-based structured prediction
Machine Learning
Sentence compression beyond word deletion
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Summarization with a joint model for sentence extraction and compression
ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing
Seed and Grow: augmenting statistically generated summary sentences using schematic word patterns
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Sentence compression as tree transduction
Journal of Artificial Intelligence Research
The noisy channel model for unsupervised word sense disambiguation
Computational Linguistics
Statistical model for Japanese abbreviations
Intelligent Data Analysis - Artificial Intelligence
Time-efficient creation of an accurate sentence fusion corpus
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Automatic generation of story highlights
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Title generation with quasi-synchronous grammar
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Discourse constraints for document compression
Computational Linguistics
Evaluating sentence compression: pitfalls and suggested remedies
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Discourse structure and language technology
Natural Language Engineering
An abstractive approach to sentence compression
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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We present a document compression system that uses a hierarchical noisy-channel model of text production. Our compression system first automatically derives the syntactic structure of each sentence and the overall discourse structure of the text given as input. The system then uses a statistical hierarchical model of text production in order to drop non-important syntactic and discourse constituents so as to generate coherent, grammatical document compressions of arbitrary length. The system outperforms both a baseline and a sentence-based compression system that operates by simplifying sequentially all sentences in a text. Our results support the claim that discourse knowledge plays an important role in document summarization.