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.

使用文献计量学文本挖掘和摘要技术对科学文献进行自动生成，利用引文生成摘要，通过 C-LexRank 对单篇文章进行摘要提取，同时拓展到一系列科学文献的摘要提取和分析，证明引文是创建摘要的一种独特信息资源。