P03-1071 |
results show a marked improvement in
|
meeting segmentation
|
with the incorporation of both
|
W10-4360 |
general processing architecture for
|
meeting segmentation
|
assumed in this paper is shown
|
N07-2019 |
. As part of our research into
|
meeting segmentation
|
and recognition , we have collected
|
W10-4360 |
2008 ) . The transcript-based
|
meeting segmentation
|
described in ( Galley et al.
|
P06-1004 |
software toolkit , developed for
|
meeting segmentation
|
( Gruenstein et al. , 2005 )
|
J12-1001 |
conversational data , such as
|
meeting segmentation
|
and summarization . For example
|
P08-1095 |
including the related task of
|
meeting segmentation
|
( Malioutov and Barzilay , 2006
|
N09-1023 |
segmentation ( Ji et al. , 2003 ) and
|
meeting segmentation
|
( Malioutov et al. , 2006 ; Malioutov
|
W10-4360 |
seen above , some approaches to
|
meeting segmentation
|
complement these basic data with
|
P08-1095 |
information which has proven useful in
|
meeting segmentation
|
( Galley et al. , 2003 ) and
|
J10-3004 |
2001 ) and the related task of
|
meeting segmentation
|
( Malioutov and Barzilay 2006
|
W12-3205 |
. But both Niekrasz ' work on
|
meeting segmentation
|
( Sec - tion 4.1 ) and the discussion
|
W10-4360 |
the best results reported in the
|
meeting segmentation
|
literature to date , namely Pk
|