  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 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.
