A learning-based approach to summarization
- Alexander Rudnicky | Carnegie Mellon University
Research in summarization has been handicapped by a lack of agreement on how to generate a standard summary (which can serve as a reference in the evaluation of alternative approaches). Part of the problem rests in disagreement between different humans on what constitutes a single good summary. We propose an alternate approach that dispenses with the need for a global reference and instead focuses on the ability of a summarizer to rapidly learn how an individual human summarizes material.
Speaker Details
Dr. Rudnicky’s research spans many areas of spoken language processing, including contributions to dialog management, language generation and recovery from misunderstanding. Dr. Rudnicky is currently a Principal Systems Scientist in the Computer Science Department at Carnegie Mellon University and on the faculty of the Language Technologies Institute. He is a recipient of the Allen Newell Award for Research Excellence. He currently serves on the boards of the Applied Voice Input-Input Society (AVIOS) and of SIGdial.
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Jeff Running
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