Topic Segmentation of Dialogue

Abstract

We introduce a novel topic segmentation approach that combines evidence of topic shifts from lexical cohesion with linguistic evidence such as syntactically distinct features of segment initial and final contributions. Our evaluation shows that this hybrid approach outperforms state-of-the-art algorithms even when applied to loosely structured, spontaneous dialogue. Further analysis reveals that using dialogue exchanges versus dialogue contributions improves topic segmentation quality.