Paper: A Two-step Approach to Sentence Compression of Spoken Utterances

ACL ID P12-2033
Title A Two-step Approach to Sentence Compression of Spoken Utterances
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

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 com- pressed sentences. In the second step, we use a discriminative training approach to cap- ture sentence level global information from the candidates and rerank them. For evalua- tion, we compare our system output with mul- tiple human references. Our results show that the new features we introduced in the first compression step improve performance upon the previous work on the same data set, and reranking is able to yield additional gain, espe- cially when training is performed to take into account multiple references.