Discourse strategies for generating natural-language text
Artificial Intelligence
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
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
Planning text for advisory dialogues: capturing intentional and rhetorical information
Computational Linguistics
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Computational Linguistics
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Natural Language Engineering
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Natural Language Engineering
Indexing and exploiting a discourse history to generate context-sensitive explanations
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Artificial Intelligence
Generating textual summaries of bar charts
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
ACM Transactions on Accessible Computing (TACCESS)
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SLPAT '11 Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies
Content selection from an ontology-based knowledge base for the generation of football summaries
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Content selection from semantic web data
INLG '12 Proceedings of the Seventh International Natural Language Generation Conference
Access to multimodal articles for individuals with sight impairments
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Generating natural language descriptions from OWL ontologies: the natural OWL system
Journal of Artificial Intelligence Research
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This paper presents an easy-to-adapt, discourse-aware framework that can be utilized as the content selection component of a generation system whose goal is to deliver descriptive texts in several turns. Our framework involves a novel use of a graph-based ranking algorithm, to iteratively determine what content to convey to a given request while taking into account various considerations such as capturing a priori importance of information, conveying related information, avoiding redundancy, and incorporating the effects of discourse history. We illustrate and evaluate this framework in an accessibility system for sight-impaired individuals.