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In a statistical language model based automated directory assistance system, extracting the salient information from the recognition output can significantly increase the accuracy of the backend listing database search. In this paper, we describe a Hidden Markov model (HMM) based saliency parser that was developed to accurately and efficiently identify salient words from the recognition output by modeling both the syntactic structure as well as the lexical distribution. The parser can be trained using a relatively small data set with coarse syntactic class labels, without the need for detailed syntactic knowledge or a treebank-like corpus. Experimental results on a research corpus of directory assistance utterances betoken the parsers importance within the automated system. The results demonstrate that the proposed saliency parser can significantly improve the overall automation rate without increasing the error rate.