Discourse strategies for generating natural-language text
Artificial Intelligence
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
Planning coherent multisentential text
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Multidocument summarization via information extraction
HLT '01 Proceedings of the first international conference on Human language technology research
Sentence Fusion for Multidocument News Summarization
Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
ACM Transactions on Speech and Language Processing (TSLP)
SimpleNLG: a realisation engine for practical applications
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Sentence compression as tree transduction
Journal of Artificial Intelligence Research
Syntax-driven sentence revision for broadcast news summarization
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Framework for abstractive summarization using text-to-text generation
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Journal of Information Science
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This paper shows that full abstraction can be accomplished in the context of guided summarization. We describe a work in progress that relies on Information Extraction, statistical content selection and Natural Language Generation. Early results already demonstrate the effectiveness of the approach.