J05-4003 high-level overview of our parallel sentence extraction system . In Section 3 , we describe
W03-1204 evaluated the performance of the sentence extraction system for Japanese lectures . At both
E09-1003 general architecture of our parallel sentence extraction system is shown in figure 3 . Starting
W03-0504 document or poor performance of the sentence extraction system . To separate these effects and
W02-0401 to be typical of those found in sentence extraction systems . Note that some of our features
W03-1204 section , we give an overview of our sentence extraction system , which uses multiple components
E03-2013 marisation . The result is the sentence extraction system shown in Figure 1 , the relevant
W03-0510 affects the average performance of sentence extraction system is the number of sentences contained
W03-1204 the three sets of data for the sentence extraction system described in the previous section
W03-0510 extractions is the upper bound that a sentence extraction system can achieve within the given
J05-4004 they can only be used to train sentence extraction systems . In the context of headline
C04-1064 useful for training and evaluating sentence extraction systems . However , it is costly to create
W00-0406 study is a version of the SRA sentence extraction system described in Aone et al. ( 1997
W03-1204 2002 ) reported the results of a sentence extraction system using an SVM , which categorized
N09-2067 presents the performance of the sentence extraction system using manual and automatic tran
W03-1204 extraction . 3 Overview of our sentence extraction system In this section , we give an
J05-4004 suitable training data only for sentence extraction systems . To train more advanced extraction
C00-1012 vary ; so do human extractors . Sentence extraction systems may be evaluated by comparing
W03-1204 That is , the performance of a sentence extraction system can be improved by categorizing
W03-0510 the performance upper bound of a sentence extraction system and the effect of compression
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