Welcome to the home page of the iOpener project, jointly run by University of Maryland and the University of Michigan.

* [2011-12-12] (NEW!) NSF Discovery story
* [2011-12-12] (NEW!) UMD ASE page
In today's rapidly expanding disciplines, scientists and scholars are constantly faced with the daunting task of keeping up with knowledge in their field. In addition, the increasingly interconnected nature of real-world tasks often requires experts in one discipline to rapidly learn about other areas in a short amount of time. Cross-disciplinary research requires scientists in such areas as linguistics, biology, and sociology to learn about computational approaches and applications. Both students and educators must have access to accurate surveys of previous work, ranging from short summaries to in-depth historical notes. Government decision-makers must learn about different scientific fields to determine funding priorities.

The goal of iOPENER (Information Organization for PENning Expositions on Research) is to generate readily-consumable surveys of different scientific domains and topics, targeted to different audiences and levels, e.g., expert specialists, scientists from related disciplines, educators, students, government decision makers, and citizens including minorities and underrepresented groups. Surveyed material is presented in different modalities, e.g., an enumerated list of articles, a bulleted list of key facts, a textual summary, or a visual presentation with zoom and filter capabilities. The original contributions of this research are in the creation of an infrastructure for automatically summarizing entire areas of scientific endeavor by linking three available technologies: (1) bibliometric lexical link mining; (2) summarization techniques; and (3) visualization tools for displaying both structure and content.

The iOPENER software and resulting surveys will be made publicly available and research results will be presented at conferences such as the ACL, SIGIR, and ASIST, as well as to broader audiences, e.g., expert specialists, students, educators, and government decision makers. Application areas include digital government, emergency response, and public health issues. The URL for this project is


This work has been partially supported by the National Science Foundation grant "iOPENER: A Flexible Framework to Support Rapid Learning in Unfamiliar Research Domains", jointly awarded to UMd and UMich as IIS 0705832.
* Papers / Technical Reports
* Datasets
* Software
Senior Personnel
* Bonnie Dorr (Professor, University of Maryland)
* Dragomir Radev (Associate Professor, University of Michigan)
* Judith Klavans (Research Professor, University of Maryland )
* Jimmy Lin (Assistant Professor, University of Maryland)
* Ben Shneiderman (Professor, University of Maryland)
Graduate Students
* Melissa Egan (Ph.D. Student, University of Maryland)
* Vahed Qazvinian (Ph.D. Student, University of Michigan)
* Pradeep Muthukrishnan (Ph.D. Student, University of Michigan)
* Cody Dunne (Ph.D. Student, University of Maryland)
* Robert Gove (Ph.D. Student, University of Maryland)
Postdoctoral Researchers
* Saif Mohammad (Postdoctoral Researcher, University of Maryland)
Other Affiliates
* Georg Apitz (Student Affiliate, University of Maryland)
* Alex Aris (Student Affiliate, University of Maryland)
* Ahmed Hassan (Student Affiliate, University of Michigan)
* Chen Huang (Student Affiliate, University of Michigan)
* David Zajic (Research Affiliate, University of Maryland)

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