Using spoken utterance compression for meeting summarization: A pilot study

Fei Liu, Yang Liu

Abstract: Most previous work on meeting summarization focused on extractive approaches; however, directly concatenating the extracted spoken utterances may not form a good summary. In this paper, we investigate if it is feasible to compress the transcribed spoken utterances and if using the compressed utterances benefits meeting summarization. We model the utterance compression task as a sequence labeling problem, and show satisfying performance using a CRF model that incorporates a variety of features capturing lexical, syntactic, and discourse information. We evaluate the impact of utterance compression on the meeting summarization task using compressed sentences (pre-compression) and original transcripts (post-compression), and find that using the compressed meeting transcripts yields slightly better summarization performance. In general, using sentence compression together with extractive summarization can generate reasonable compressed summaries. This is a step closer to abstractive summarization.