Text generation: using discourse strategies and focus constraints to generate natural language text
Text generation: using discourse strategies and focus constraints to generate natural language text
Attention, intentions, and the structure of discourse
Computational Linguistics
Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
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
The rhetorical parsing of natural language texts
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
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The role of centering theory's rough-shift in the teaching and evaluation of writing skills
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Centering: A Parametric Theory and Its Instantiations
Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Computing locally coherent discourses
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Modeling local coherence: An entity-based approach
Computational Linguistics
Automatically generating Wikipedia articles: a structure-aware approach
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Content modeling using latent permutations
Journal of Artificial Intelligence Research
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Since the early days of generation research, it has been acknowledged that modeling the global structure of a document is crucial for producing coherent, readable output. However, traditional knowledge-intensive approaches have been of limited utility in addressing this problem since they cannot be effectively scaled to operate in domain-independent, large-scale applications. Due to this difficulty, existing text-to-text generation systems rarely rely on such structural information when producing an output text. Consequently, texts generated by these methods do not match the quality of those written by humans - they are often fraught with severe coherence violations and disfluencies. In this chapter, I will present probabilistic models of document structure that can be effectively learned from raw document collections. This feature distinguishes these new models from traditional knowledge intensive approaches used in symbolic concept-to-text generation. Our results demonstrate that these probabilistic models can be directly applied to content organization, and suggest that these models can prove useful in an even broader range of text-to-text applications than we have considered here.