Citation Handling for Improved Summarization of Scientific Documents

Abstract

In this paper we present the first steps toward improving summarization of scientific documents through citation analysis and parsing. Prior work (Mohammad et al., 2009) argues that citation texts (sentences that cite other papers) play a crucial role in automatic summarization of a topical area, but did not take into account the noise introduced by the citations themselves. We demonstrate that it is possible to improve summarization output through careful handling of these citations. We base our experiments on the application of an improved trimming approach to summarization of citation texts extracted from Question-Answering and Dependency-Parsing documents. We demonstrate that confidence scores from the Stanford NLP Parser (Klein and Manning, 2003) are significantly improved, and that Trimmer (Zajic et al., 2007), a sentence-compression tool, is able to generate higher-quality candidates. Our summarization output is currently used as part of a larger system, Action Science Explorer (ASE) (Gove, 2011).