Citation Handling for Improved Summarization of Scientific Documents

In this paper we present the first steps toward improving summarizationof 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).