SALIENT SENTENCE IDENTIFICATION IN TEXT

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-12-M-0397
Agency Tracking Number: O11B-TD1-4005
Amount: $100,000.00
Phase: Phase I
Program: STTR
Awards Year: 2012
Solicitation Year: 2011
Solicitation Topic Code: OSD11-TD1
Solicitation Number: 2011.B
Small Business Information
8260 Greensboro Drive, Suite 600, McLean, VA, -
DUNS: 081093775
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Alan Breitler
 Principal Investigator
 (301) 649-5031
 alan.breitler@ddlomni.com
Business Contact
 Nancy Doolin
Title: Corporate Contracting Officer
Phone: (703) 918-4335
Email: nancy.doolin@ddlomni.com
Research Institution
 Virginia Tech
 Chris North
 Dept of Computer Science
2202 Kraft Drive
Blacksburg, VA, 24061-0106
 (540) 231-2458
 Nonprofit college or university
Abstract
DDL OMNI Engineering and Virginia Polytechnic Institute and State University will use our Salient Sentence Identification in Text (S2I2T) algorithm for the automated identification and characterization of the salient context related content in text documents. Our approach uniquely identifies salience from sentences within the documents, producing an easy to read result. Further, our approach allows the user to define the context directly at execution of the algorithm to match user entered terms against sentences identified as salient. During Phase I our approach"s effectiveness will be validated via experiments comparing algorithmic results to human recognized salience. Additionally, we will continue refinements to our S2I2T prototype to improve algorithm performance and statistically validate the performance improvements.

* Information listed above is at the time of submission. *

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