file.pdf (224.41 kB)

Two-layer mutually reinforced random walk for improved multi-party meeting summarization

Download (224.41 kB)
journal contribution
posted on 01.12.2012 by Yun-Nung Chen, Florian Metze

This paper proposes an improved approach of summarization for spoken multi-party interaction, in which a two-layer graph with utterance-to-utterance, speaker-to-speaker, and speaker-to-utterance relations is constructed. Each utterance and each speaker are represented as a node in the utterance-layer and speaker-layer of the graph respectively, and the edge between two nodes is weighted by the similarity between the two utterances, the two speakers, or the utterance and the speaker. The relation between utterances is evaluated by lexical similarity via word overlap or topical similarity via probabilistic latent semantic analysis (PLSA). 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 same speaker and similar utterances. For both ASR output and manual transcripts, experiments confirmed the efficacy of involving speaker information in the two-layer graph for summarization.


Publisher Statement

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works