Effective pseudo-relevance feedback for language modeling in extractive speech summarization

Shih-Hung Liu, Kuan-Yu Chen, Yu-Lun Hsieh, Berlin Chen, Hsin-Min Wang, Hsu-Chun Yen, Wen-Lian Hsu
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
more » ... eliminary 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.
doi:10.1109/icassp.2014.6854196 dblp:conf/icassp/LiuCHCWYH14 fatcat:bzkrabnhozc4zbu3jhl3ceq4ha