This paper presents a two-step approach to compress spontaneous spoken utterances. In the first step, we use a sequence labeling method to determine if a word in the utterance can be removed, and generate n-best compressed sentences. In the second step, we use a discriminative training approach to capture sentence level global information from the candidates and rerank them.