DocumentCode :
3427056
Title :
Impact of automatic sentence segmentation on meeting summarization
Author :
Liu, Yang ; Xie, Shasha
Author_Institution :
Univ. of Texas at Dallas, Richardson, TX
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
5009
Lastpage :
5012
Abstract :
This paper investigates the impact of automatic sentence segmentation on speech summarization using the ICSI meeting corpus. We use a hidden Markov model (HMM) for sentence segmentation that integrates the N-gram language model and pause information, and a maximum marginal relevance (MMR) based extractive summarization method. The system-generated summaries are compared to multiple human summaries using the ROUGE scores. The decision thresholds from the segmentation system are varied to examine the impact of different segments on summarization. We find that (1) using system generated utterance segments degrades summarization performance compared to using human annotated sentences; (2) segmentation needs to be optimized for summarization instead of the segmentation task itself, however, the patterns are slightly different from prior work for other tasks such as parsing; and (3) there are effects from different summarization evaluation metrics as well as speech recognition errors.
Keywords :
hidden Markov models; speech processing; speech recognition; HMM; ICSI meeting corpus; N-gram language model; ROUGE scores; automatic sentence segmentation; decision thresholds; extractive summarization method; hidden Markov model; human annotated sentences; maximum marginal relevance; meeting summarization; pause information; speech recognition errors; speech summarization; Broadcasting; Data mining; Degradation; Ear; Hidden Markov models; Humans; Natural languages; Speech recognition; Statistics; Testing; MMR; ROUGE; meeting summarization; sentence segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4518783
Filename :
4518783
Link To Document :
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