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...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Liangda Li, Ke Zhou, Gui-Rong Xue, Hongyuan Zha, Yong Yu
• Keywords: Document Summarization, Extract-based Summarization Problem, Internet Technology, Performance Keywords Summarization, WWW 2009