Generating Extractive Summaries of Scientific Paradigms

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 sh...

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Bibliographic Details
Published in:The Journal of artificial intelligence research 2013-01, Vol.46, p.165-201
Main Authors: Qazvinian, V., Radev, D. R., Mohammad, S. M., Dorr, B., Zajic, D., Whidby, M., Moon, T.
Format: Article
Language:English
Subjects:
Artificial intelligence
Bibliometrics
Data mining
Scientific papers
Summaries
Citations: Items that cite this one
Online Access:Get full text
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Summary: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.
ISSN:1076-9757
1076-9757
1943-5037
DOI:10.1613/jair.3732