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

This paper proposes an improved approach to extrac-tive summarization of spoken multi-party interac-tion, 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 topi-cal similarity between the utterances, evaluated us-ing 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 ex-periments 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 impor-tant utterances.