Integrating Intra-Speaker Topic Modeling and Temporal-Based Inter-Speaker Topic Modeling in Random Walk for Improved Multi-Party Meeting Summarization

This paper proposes an improved approach of summarization for spoken multi-party interaction, in which intra-speaker and inter-speaker topics are modeled in a graph constructed with topical relations. Each utterance is represented as a node of the graph, and the edge between two nodes is weighted by the similarity between the two utterances, which is the topical similarity, as evaluated by probabilistic latent semantic analysis (PLSA). We model intra-speaker topics by sharing the topics from the same speaker and inter-speaker topics by partially sharing the topics from the adjacent utterances based on temporal information. For both manual transcripts and ASR output, experiments confirmed the efficacy of combining intra- and inter-speaker topic modeling for summarization.