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Effective pseudo-relevance feedback for language modeling in extractive speech summarization
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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 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
doi:10.1109/icassp.2014.6854196
dblp:conf/icassp/LiuCHCWYH14
fatcat:bzkrabnhozc4zbu3jhl3ceq4ha