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首页> 外文会议>Annual conference of the International Speech Communication Association >Integrating Intra-Speaker Topic Modeling and Temporal-Based Inter-Speaker Topic Modeling in Random Walk for Improved Multi-Party Meeting Summarization
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Integrating Intra-Speaker Topic Modeling and Temporal-Based Inter-Speaker Topic Modeling in Random Walk for Improved Multi-Party Meeting Summarization

机译：集成发言者主题模型和基于时间的发言者间主题模型在随机行走中的功能，以改进多方会议摘要

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摘要

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.
机译：本文提出了一种改进的语音多方交互摘要方法，其中，将说话者内和说话者间的主题建模在具有主题关系的图表中。每个话语都表示为图的一个节点，并且通过概率性潜在语义分析（PLSA）评估的两个话语之间的相似性（即主题相似性）对两个节点之间的边缘进行加权。我们通过共享来自同一说话者的主题来建模说话者内部的主题，并通过基于时间信息部分共享来自相邻话语的主题来对说话者之间的主题进行建模。对于手动笔录和ASR输出，实验证实了将说话者内部和说话者之间的话题建模相结合进行汇总的功效。

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