Automatically learning cognitive status for multi-document summarization of newswire

Machine summaries can be improved by using knowledge about the cognitive status of news article referents. In this paper, we present an approach to automatically acquiring distinctions in cognitive status using machine learning over the forms of referring expressions appearing in the input. We focus on modeling references to people, both because news often revolve around people and because existing natural language tools for named entity identification are reliable. We examine two specic distinctions—whether a person in the news can be assumed to be known to a target audience (hearer-old vs hearer-new) and whether a person is a major character in the news story. We report on machine learning experiments that show that these distinctions can be learned with high accuracy, and validate our approach using human subjects.