CAREER: Content and Cohesion Models, with Applications to Text Summarization and Natural Language Generation

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Within the last decade, probabilistic methods have delivered successful analyses of natural language texts that, in turn, have enabled a broad range of valuable and practical applications, such as machine translation, question answering, and summarization. Despite this success, existing methods suffer from a fundamental limitation: they process each document with little or no ability to take advantage of its global structure. All too often, this results in suboptimal performance for the task at hand.The goal of this project is to develop probabilistic models for two fundamental, orthogonal dimensions of text, content and cohesion. A model based on the first dimension, content, describes the topics present in a text and their organization. The second dimension, cohesion, is concerned with how information is realized in a given text. Development of these models requires new unsupervised techniques able to capture complex text properties and novel algorithms for topical discretization and discourse grammar induction.Gaining a computational measure of what constitutes a good text will open new research avenues on the edge of humanities and computer science. Probabilistic text models will form a basis for novel approaches to text summarization and generation that will make on-line information much more accessible than is currently the case. This will substantially affect the way people experience the many forms of textual on-line information, including news reports, consumer health information, and government documents. Students will become involved in this research through hands-on projects, outreach programs, and courses at both the undergraduate and graduate level.