TY - GEN
T1 - Effective pseudo-relevance feedback for language modeling in extractive speech summarization
AU - Liu, Shih Hung
AU - Chen, Kuan Yu
AU - Hsieh, Yu Lun
AU - Chen, Berlin
AU - Wang, Hsin Min
AU - Yen, Hsu Chun
AU - Hsu, Wen Lian
PY - 2014
Y1 - 2014
N2 - Extractive speech summarization, aiming to automatically select an indicative set of sentences from a spoken document so as to concisely represent the most important aspects of the document, has become an active area for research and experimentation. An emerging stream of work is to employ the language modeling (LM) framework along with the Kullback-Leibler divergence measure for extractive speech summarization, which can perform important sentence selection in an unsupervised manner and has shown preliminary success. This paper presents a continuation of such a general line of research and its main contribution is two-fold. First, by virtue of pseudo-relevance feedback, we explore several effective sentence modeling formulations to enhance the sentence models involved in the LM-based summarization framework. Second, the utilities of our summarization methods and several widely-used methods are analyzed and compared extensively, which demonstrates the effectiveness of our methods.
AB - Extractive speech summarization, aiming to automatically select an indicative set of sentences from a spoken document so as to concisely represent the most important aspects of the document, has become an active area for research and experimentation. An emerging stream of work is to employ the language modeling (LM) framework along with the Kullback-Leibler divergence measure for extractive speech summarization, which can perform important sentence selection in an unsupervised manner and has shown preliminary success. This paper presents a continuation of such a general line of research and its main contribution is two-fold. First, by virtue of pseudo-relevance feedback, we explore several effective sentence modeling formulations to enhance the sentence models involved in the LM-based summarization framework. Second, the utilities of our summarization methods and several widely-used methods are analyzed and compared extensively, which demonstrates the effectiveness of our methods.
KW - Kullback-Leibler divergence
KW - Speech summarization
KW - language modeling
KW - pseudo-relevance feedback
UR - http://www.scopus.com/inward/record.url?scp=84905234272&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905234272&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854196
DO - 10.1109/ICASSP.2014.6854196
M3 - Conference contribution
AN - SCOPUS:84905234272
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3226
EP - 3230
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -