ISCA Archive Interspeech 2009
ISCA Archive Interspeech 2009

Clusterrank: a graph based method for meeting summarization

Nikhil Garg, Benoit Favre, Korbinian Reidhammer, Dilek Hakkani-Tür

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, ill-formed 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.

doi: 10.21437/Interspeech.2009-456

Cite as: Garg, N., Favre, B., Reidhammer, K., Hakkani-Tür, D. (2009) Clusterrank: a graph based method for meeting summarization. Proc. Interspeech 2009, 1499-1502, doi: 10.21437/Interspeech.2009-456

  author={Nikhil Garg and Benoit Favre and Korbinian Reidhammer and Dilek Hakkani-Tür},
  title={{Clusterrank: a graph based method for meeting summarization}},
  booktitle={Proc. Interspeech 2009},