A Statistical Approach to Automatic Speech Summarization
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A Statistical Approach to Automatic Speech Summarization Chiori Hori Department of Computer Science, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan Email: [email protected]
Sadaoki Furui Department of Computer Science, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan Email: [email protected]
Rob Malkin Interactive Systems Labs, Carnegie Mellon University, Pittsburgh, PA 15213, USA Email: [email protected]
Hua Yu Interactive Systems Labs, Carnegie Mellon University, Pittsburgh, PA 15213, USA Email: [email protected]
Alex Waibel Interactive Systems Labs, Carnegie Mellon University, Pittsburgh, PA 15213, USA Email: [email protected] Received 20 March 2002 and in revised form 11 November 2002 This paper proposes a statistical approach to automatic speech summarization. In our method, a set of words maximizing a summarization score indicating the appropriateness of summarization is extracted from automatically transcribed speech and then concatenated to create a summary. The extraction process is performed using a dynamic programming (DP) technique based on a target compression ratio. In this paper, we demonstrate how an English news broadcast transcribed by a speech recognizer is automatically summarized. We adapted our method, which was originally proposed for Japanese, to English by modifying the model for estimating word concatenation probabilities based on a dependency structure in the original speech given by a stochastic dependency context free grammar (SDCFG). We also propose a method of summarizing multiple utterances using a two-level DP technique. The automatically summarized sentences are evaluated by summarization accuracy based on a comparison with a manual summary of speech that has been correctly transcribed by human subjects. Our experimental results indicate that the method we propose can effectively extract relatively important information and remove redundant and irrelevant information from English news broadcasts. Keywords and phrases: speech summarization, summarization scores, two-level dynamic programming, stochastic dependency context free grammar, summarization accuracy.
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INTRODUCTION
The revolutionary increases in the computing power and storage capacity have enabled an enormous amount of speech data, or multimedia data that includes speech, to be managed as an information source. The next step is to create a system in which speech data is tagged (annotated) by text allowing information to be retrieved and extracted from such
databases. Multimedia databases including indexes can be automatically constructed using speech-recognition systems. Speech can be broadcast with captions generated by speechrecognition systems and simultaneously saved in speech and text (i.e., captions) archives in a database. Captioning can be considered a form of indexing accessible by individual words in the whole speech. One approach attempted to extract information from such a database by tracking speech through
A Statistical
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