document: Enhancing diversity , coverage and balance for summarization through structure learning Document summarization plays an increasingly important role with the exponential growth of documents on the Web . Many supervised and unsupervised approaches have been proposed to generate summaries from documents . However , these approaches seldom simultaneously consider summary diversity , coverage , and balance issues which to a large extent determine the quality of summaries . In this paper , we consider extract-based summarization emphasizing the following three requirements : 1 ) diversity in summarization , which seeks to reduce redundancy among sentences in the summary ; 2 ) sufficient coverage , which focuses on avoiding the loss of the document 's main information when generating the summary ; and 3 ) balance , which demands that different aspects of the document need to have about the same relative importance in the summary . We formulate the extract-based summarization problem as learning a mapping from a set of sentences of a given document to a subset of the sentences that satisfies the above three requirements . The mapping is learned by incorporating several constraints in a structure learning framework , and we explore the graph structure of the output variables and employ structural SVM for solving the resulted optimization problem . Experiments on the DUC2001 data sets demonstrate significant performance improvements in terms of F1 and ROUGE metrics .
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