Generating extractive summaries of scientific paradigms

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Journal titleJournal of Artificial Intelligence Research
Pages165201; # of pages: 37
SubjectAutomatically generated; Community detection; Dependency parsing; Large amounts; Scientific articles; Scientific literature; Scientific paradigm; Set of questions; Data mining; Dynamic positioning; Natural language processing systems
AbstractResearchers 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 scientific 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.
Publication date
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21270468
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Record identifier6ff31eea-c26d-4f61-975a-39229415d238
Record created2014-02-12
Record modified2016-05-09
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