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Discourse segmentation of multi-party conversation
Discourse Segmentation of Multi-Party Conversation
Michel Galley Kathleen McKeown
Columbia University
Computer Science Department
1214 Amsterdam Avenue
New York, NY 10027, USA
{galley,kathy}@
Eric Fosler-Lussier
Columbia University
Electrical Engineering Department
500 West 120th Street
New York, NY 10027, USA
fosler@
Hongyan Jing
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598, USA
hjing@
Abstract
We present a domain-independent topic
segmentation algorithm for multi-party
speech. Our feature-based algorithm com-
bines knowledge about content using a
text-based algorithm as a feature and
about form using linguistic and acous-
tic cues about topic shifts extracted from
speech. This segmentation algorithm uses
automatically induced decision rules to
combine the different features. The em-
bedded text-based algorithm builds on lex-
ical cohesion and has performance compa-
rable to state-of-the-art algorithms based
on lexical information. A significant er-
ror reduction is obtained by combining the
two knowledge sources.
1 Introduction
Topic segmentation aims to automatically divide text
documents, audio recordings, or video segments,
into topically related units. While extensive research
has targeted the problem of topic segmentation of
written texts and spoken monologues, few have stud-
ied the problem of segmenting conversations with
many participants (e.g., meetings). In this paper, we
present an algorithm for segmenting meeting tran-
scripts. This study uses recorded meetings of typi-
cally six to eight participants, in which the informal
style includes ungrammatical sentences and overlap-
ping speakers. These meetings generally do not have
pre-set agendas, and the topics discussed in the same
meeting may or may not related.
The meeting segmenter comprises two compo-
nents: one that capitalizes on word distribution to
identify homogeneous units that are topically cohe-
sive, and a second component that analyzes conver-
sational features of meeting trans
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