Statistics-Based Summarization -- Step One: Sentence Compression

Kevin Knight and Daniel Marcu, University of Southern California

We discuss the problem of generating text that preserves certain ambiguities, a capability that is useful in applications such as machine translation. We show that it is relatively simple to extend a hybrid symbolic/statistical generator to do ambiguity preservation. The paper gives algorithms and examples, and it discusses practical linguistic difficulties that arise in ambiguity preservation.


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