A global optimization framework for meeting summarization

Authors:
Dan Gillick;Korbinian Riedhammer;Benoit Favre;Dilek Hakkani-Tur
Affiliations:
Computer Science Dept., University of California Berkeley, USA;Computer Science Dept. 5, University of Erlangen-Nuremberg, GERMANY;International Computer Science Institute, Berkeley, USA;International Computer Science Institute, Berkeley, USA
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

We introduce a model for extractive meeting summarization based on the hypothesis that utterances convey bits of information, or concepts. Using keyphrases as concepts weighted by frequency, and an integer linear program to determine the best set of utterances, that is, covering as many concepts as possible while satisfying a length constraint, we achieve ROUGE scores at least as good as a ROUGE-based oracle derived from human summaries. This brings us to a critical discussion of ROUGE and the future of extractive meeting summarization.