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Extractive summarization of multi-party meetings through discourse segmentation

Published online by Cambridge University Press:  04 March 2015

MOHAMMAD HADI BOKAEI ,
HOSSEIN SAMETI  and
YANG LIU
MOHAMMAD HADI BOKAEI
Affiliation:
Speech Processing Lab, Computer Engineering Department, Sharif University of Technology, Tehran, I.R. Iran e-mail: bokaei@ce.sharif.edu, sameti@sharif.edu Human Language Technology Group, Computer Science Department, The University of Texas at Dallas, Richardson, TX, USA e-mail: yangl@hlt.utdallas.edu
HOSSEIN SAMETI
Affiliation:
Speech Processing Lab, Computer Engineering Department, Sharif University of Technology, Tehran, I.R. Iran e-mail: bokaei@ce.sharif.edu, sameti@sharif.edu
YANG LIU
Affiliation:
Human Language Technology Group, Computer Science Department, The University of Texas at Dallas, Richardson, TX, USA e-mail: yangl@hlt.utdallas.edu
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Abstract

In this article we tackle the problem of multi-party conversation summarization. We investigate the role of discourse segmentation of a conversation on meeting summarization. First, an unsupervised function segmentation algorithm is proposed to segment the transcript into functionally coherent parts, such as Monologuei (which indicates a segment where speaker i is the dominant speaker, e.g., lecturing all the other participants) or Discussionx1x2, . . ., xn (which indicates a segment where speakers x1 to xn involve in a discussion). Then the salience score for a sentence is computed by leveraging the score of the segment containing the sentence. Performance of our proposed segmentation and summarization algorithms is evaluated using the AMI meeting corpus. We show better summarization performance over other state-of-the-art algorithms according to different metrics.

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Articles
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Natural Language Engineering , Volume 22 , Issue 1 , January 2016 , pp. 41 - 72
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Copyright © Cambridge University Press 2015 

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