A Two-step Approach to Sentence Compression of Spoken Utterances

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. For evaluation, we compare our system output with multiple 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, especially when training is performed to take into account multiple references.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Dong Wang, Xian Qian, Yang Liu
• Keywords: ACL 2012, Computational Linguistics, Sentence Level, Spoken Utterances, Step Approach