This paper presents a graph-based model that integrates prosodic features into an\u00a0unsupervised speech summarization framework without any lexical information. In particular it builds on previous work using mutually reinforced random walks, in which a two-layer\u00a0graph structure is used to select the most salient utterances of a conversation. The model\u00a0consists of one layer of utterance nodes and another layer of prosody nodes. The random\u00a0walk algorithm propagates scores between layers to use shared information for selecting\u00a0utterance nodes with highest scores as summaries. A comparative evaluation
\nof our prosody-based model against several baselines on a corpus of academic multi-party meetings reveals that it performs competitively on very short summaries, and better on longer summaries according to ROUGE scores as well as the average relevance of selected utterances.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents a graph-based model that integrates prosodic features into an\u00a0unsupervised speech summarization framework without any lexical information. In particular it builds on previous work using mutually reinforced random walks, in which a two-layer\u00a0graph structure is used to select the most salient utterances of a conversation. 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