Accession Number:

AD1043087

Title:

Generating Extractive Summaries of Scientific Paradigms (Open Access, Publisher's Version)

Descriptive Note:

Journal Article - Open Access

Corporate Author:

University of Michigan Ann Arbor United States

Personal Author(s):

• Qazvinian, Vahed
• Radev,Dragomir R.
• Mohammad,Saif M.
• Dorr,Bonnie
• Zajic,David
• Whidby,Michael
• Moon,Taesun

Report Date:

2013-02-01

Pagination or Media Count:

37.0

Abstract:

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.

Descriptors:

• data mining
• AUTOMATED TEXT SUMMARIZATION
• extraction

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE