Multi-Layer Mutually Reinforced Random Walk with Hidden Parameters for Improved Multi-Party Meeting Summarization

This paper proposes an improved approach of summarization for spoken multi-party interaction, in which a multi-layer graph with hidden parameters is constructed. The graph includes utterance-to-utterance relation, utterance-to-parameter weight, and speaker-to-parameter weight. Each utterance and each speaker are represented as a node in the utterance-layer and speaker-layer of the graph respectively. We use terms/ topics as hidden parameters for estimating utterance-to-parameter and speaker-to-parameter weight, and compute topical similarity between utterances as the utterance-to-utterance relation. By within- and between-layer propagation in the graph, the scores from different layers can be mutually reinforced so that utterances can automatically share the scores with the utterances from the speakers who focus on similar terms/ topics. For both ASR output and manual transcripts, experiments confirmed the efficacy of including hidden parameters and involving speaker information in the multi-layer graph for summarization. We find that choosing latent topics as hidden parameters significantly reduces computational complexity and does not hurt the performance.