Paper: Semantic Similarity Applied To Spoken Dialogue Summarization

ACL ID C04-1110
Title Semantic Similarity Applied To Spoken Dialogue Summarization
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

We present a novel approach to spoken dialogue summarization. Our system employs a set of semantic similarity metrics using the noun por- tion of WordNet as a knowledge source. So far, the noun senses have been disambiguated man- ually. The algorithm aims to extract utterances carrying the essential content of dialogues. We evaluate the system on 20 Switchboard dia- logues. The results show that our system out- performs LEAD, RANDOM and TF*IDF base- lines.