Generating extractive summaries of scientific paradigms

  • Authors:
  • Vahed Qazvinian;Dragomir R. Radev;Saif M. Mohammad;Bonnie Dorr;David Zajic;Michael Whidby;Taesun Moon

  • Affiliations:
  • Department of EECS, University of Michigan, Ann Arbor, MI;Department of EECS & School of Information, University of Michigan, Ann Arbor, MI;National Research Council Canada, Ottawa, Ontario, Canada;Institute for Advanced Computer Studies & Computer Science, University of Maryland, College Park, MD;Institute for Advanced Computer Studies & Computer Science, University of Maryland, College Park, MD;Institute for Advanced Computer Studies & Computer Science, University of Maryland, College Park, MD;Institute for Advanced Computer Studies & Computer Science, University of Maryland, College Park, MD

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 2013

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Abstract

Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Next, we further extend our experiments to summarize a set of papers, which cover the same scienti fic topic. We generate extractive summaries of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique information amenable to creating a summary.