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  1. Natural language generation of biomedical argumentation for lay audiences.Nancy Green, Rachael Dwight, Kanyamas Navoraphan & Brian Stadler - 2011 - Argument and Computation 2 (1):23 - 50.
    This article presents an architecture for natural language generation of biomedical argumentation. The goal is to reconstruct the normative arguments that a domain expert would provide, in a manner that is transparent to a lay audience. Transparency means that an argument's structure and functional components are accessible to its audience. Transparency is necessary before an audience can fully comprehend, evaluate or challenge an argument, or re-evaluate it in light of new findings about the case or changes in scientific knowledge. The (...)
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  • Automatic generation of textual summaries from neonatal intensive care data.François Portet, Ehud Reiter, Albert Gatt, Jim Hunter, Somayajulu Sripada, Yvonne Freer & Cindy Sykes - 2009 - Artificial Intelligence 173 (7-8):789-816.
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  • “Your risk is low, because …”: argument-driven online genetic counselling.Uwe Hartung, Sara Rubinelli & Peter J. Schulz - 2010 - Argument and Computation 1 (3):199-214.
    Advances in genetic research have created the need to inform consumers. Yet, the communication of hereditary risk and of the options for how to deal with it is a difficult task. Due to the abstract nature of genetics, people tend to overestimate or underestimate their risk. This paper addresses the issue of how to communicate risk information on hereditary breast and ovarian cancer through an online application. The core of the paper illustrates the design of OPERA, a risk assessment instrument (...)
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  • Generating and evaluating evaluative arguments.Giuseppe Carenini & Johanna D. Moore - 2006 - Artificial Intelligence 170 (11):925-952.
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