ClusterRank: A Graph Based Method for Meeting Summarization

This paper presents an unsupervised, graph based approach for extractive summarization of meetings. Graph based methods such as TextRank have been used for sentence extraction from news articles. These methods model text as a graph with sentences as nodes and edges based on word overlap. A sentence node is then ranked according to its similarity with other nodes. The spontaneous speech in meetings leads to incomplete, illformed sentences with high redundancy and calls for additional measures to extract relevant sentences. We propose an extension of the TextRank algorithm that clusters the meeting utterances and uses these clusters to construct the graph. We evaluate this method on the AMI meeting corpus and show a significant improvement over TextRank and other baseline methods.


Year:
2009
Publisher:
P.O. Box 592, CH-1920 Martigny, Switzerland, Idiap
Laboratories:




 Record created 2010-02-11, last modified 2018-03-17

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