Sentence ordering for multi-document summarization in response to multiple queries

The growing access to large amounts of text data opens more opportunities in information processing. Given a list of complex questions and a set of relevant documents, the task of producing an informative and coherent summary of those documents in response to the questions has attracted a great deal of attention recently. However, the problem of organizing information for summarization so that the generated summary is coherent has received relatively little attention. Several approaches have been proposed for sentence ordering in single-document and generic multiple-document summarization, but no single method has been found to address sentence ordering in query-based summarization. In this thesis, we propose and implement an algorithm that combines constraints from query order and topical relatedness in human produced summaries of multiple documents in response to multiple questions. To test the effectiveness of the constraints, we construct a new query-based corpus from the human produced summaries for the Document Understanding Conference(DUC) 2006 evaluation. We then conduct experiments, using an automatic evaluation method based on Kendall's Tau, to evaluate and compare the effectiveness of our approaches to others. Our results show that both query order and topical relatedness improve the ordering performance when compared to a baseline method, and a combination of these two constraints achieves even better results.