Putting the user in the loop: interactive Maximal Marginal Relevance for query-focused summarization

Authors:
Jimmy Lin;Nitin Madnani;Bonnie J. Dorr
Affiliations:
University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

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Abstract

This work represents an initial attempt to move beyond "single-shot" summarization to interactive summarization. We present an extension to the classic Maximal Marginal Relevance (MMR) algorithm that places a user "in the loop" to assist in candidate selection. Experiments in the complex interactive Question Answering (ciQA) task at TREC 2007 show that interactively-constructed responses are significantly higher in quality than automatically-generated ones. This novel algorithm provides a starting point for future work on interactive summarization.