Probabilistic approaches for modeling text structure and their application to text-to-text generation

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.National Science Foundation (U.S.) (CAREER grant IIS- 0448168)Microsoft Research. New Faculty Fellowshi

Generating Tailored Textual Summaries from Ontologies

Abstract. This paper presents the ONTOSUM system which uses Natural Language Generation (NLG) techniques to produce textual summaries from Semantic Web ontologies. The main contribution of this work is in showing how existing NLG tools can be adapted to Semantic Web ontologies, in a way which minimises the customisation effort while offering more diverse output than template-based ontology verbalisers. A novel dimension of this work is the focus on tailoring the summary formatting and length according to a device profile (e.g., mobile phone, Web browser). Another innovative idea is the use of ontology mapping for summary generation from different ontologies.

Customization in a unified framework for summarizing medical literature

10.1016/j.artmed.2004.07.018Artificial Intelligence in Medicine332179-198AIME

Experiments on generating questions about facts

This paper presents an approach to the problem of factual Question Generation. Factual questions are questions whose answers are specific facts: who?, what?, where?, when?. We enhanced a simple attribute-value (XML) language and its interpretation engine with context-sensitive primitives and added a linguistic layer deep enough for the overall system to score well on user satisfiability and the \u27linguistically well-founded\u27 criteria used to measure up language generation systems. Experiments with open-domain question generation on TREC-like data validate our claims and approach. © Springer-Verlag Berlin Heidelberg 2007