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 ROUGEbased oracle derived from human summaries. This brings us to a critical discussion of ROUGE and the future of extractive meeting summarization.
REF introduce a conceptbased global optimization framework by using integer linear programming (ILP).
A global optimization framework for meeting summarization
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Computer Science