13th Annual Conference of the International Speech Communication Association

Portland, OR, USA
September 9-13, 2012

Supervised Spoken Document Summarization jointly Considering Utterance Importance and Redundancy by Structured Support Vector Machine

Hung-yi Lee (1), Yu-yu Chou (1), Yow-Bang Wang (2), Lin-shan Lee (1,2)

(1) Graduate Institute of Communication Engineering; (2) Graduate Institute of Electrical Engineering;
National Taiwan University, Taiwan

In extractive spoken document summarization, it is desired to select important utterances from documents to construct the summary while avoiding redundancy among the selected utterances, but it is not easy to balance the two different goals. In this paper, a supervised spoken document summarization approach is proposed based on structured support vector machine (SVM), in which the above two goals are jointly considered during training. A set of parameters not only describing the ways to evaluate the importance of the utterances but minimizing the redundancy is directly learned from the training set. Encouraging results were obtained on a lecture corpus in the preliminary experiments.

Index Terms: speech summarization, structured SVM

Full Paper

Bibliographic reference.  Lee, Hung-yi / Chou, Yu-yu / Wang, Yow-Bang / Lee, Lin-shan (2012): "Supervised spoken document summarization jointly considering utterance importance and redundancy by structured support vector machine", In INTERSPEECH-2012, 2342-2345.