Intra-Speaker Topic Modeling for Improved Multi-Party Meeting Summarization with Integrated Random Walk

This paper proposes an improved approach to extractive summarization of spoken multi-party interaction, in which integrated random walk is performed on a graph constructed on topical/ lexical relations. Each utterance is represented as a node of the graph, and the edges’ weights are computed from the topical similarity between the utterances, evaluated using probabilistic latent semantic analysis (PLSA), and from word overlap. We model intra-speaker topics by partially sharing the topics from the same speaker in the graph. In this paper, we perform experiments on automatically and manually generated transcripts. For automatic transcripts, our results show that intra-speaker topic sharing and integrating topical/ lexical relations can help include the important utterances.