Campus Event Calendar
Event Entry
What and Who
ClusterRank: A graph based method for meeting summarization
Nikhil Garg
Ecoles Polytechnique Fédérale de Lausanne
Talk
AG 1
AG Audience
English
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Date, Time and Location
Wednesday, 10 June 2009
14:00
30 Minutes
E1 4
Rotunde D5 - 4th floor
Saarbrücken
Abstract
In this talk, I will present an unsupervised, graph based approach for
extractive summarization of meeting transcripts. Graph based methods
such as TextRank have been used for sentence extraction in structured
text like news articles. Text was modeled as a graph with sentences as
nodes and edges based on word overlap. A sentence node was then ranked
according to its similarity with the rest of the nodes. The spontaneous
speech in meetings leads to incomplete, ill-formed sentences and high
redundancy which calls for additional measures to extract relevant
sentences. I will describe an extension of the TextRank algorithm that
segments the meeting transcript into clusters and uses these clusters to
construct the graph. The evaluation is on the AMI meeting corpus and the
results show a significant improvement over TextRank and some other
baseline methods.
extractive summarization of meeting transcripts. Graph based methods
such as TextRank have been used for sentence extraction in structured
text like news articles. Text was modeled as a graph with sentences as
nodes and edges based on word overlap. A sentence node was then ranked
according to its similarity with the rest of the nodes. The spontaneous
speech in meetings leads to incomplete, ill-formed sentences and high
redundancy which calls for additional measures to extract relevant
sentences. I will describe an extension of the TextRank algorithm that
segments the meeting transcript into clusters and uses these clusters to
construct the graph. The evaluation is on the AMI meeting corpus and the
results show a significant improvement over TextRank and some other
baseline methods.
Contact
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Jennifer Gerling, 06/05/2009 16:03 -- Created document.