MMIE training of large vocabulary recognition systems
Speech Communication
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Automatic generation of concise summaries of spoken dialogues in unrestricted domains
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Statistics-Based Summarization - Step One: Sentence Compression
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Advances in meeting recognition
HLT '01 Proceedings of the first international conference on Human language technology research
A new approach to automatic speech summarization
IEEE Transactions on Multimedia
Spoken interactive ODQA system: SPIQA
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Toward Robust Speech Recognition and Understanding
Journal of VLSI Signal Processing Systems
Semi-automated logging of contact center telephone calls
Proceedings of the 17th ACM conference on Information and knowledge management
From extractive to abstractive meeting summaries: can it be done by sentence compression?
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Dependency tree based sentence compression
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Multi-sentence compression: finding shortest paths in word graphs
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Why read if you can skim: towards enabling faster screen reading
Proceedings of the International Cross-Disciplinary Conference on Web Accessibility
Accessible skimming: faster screen reading of web pages
Proceedings of the 25th annual ACM symposium on User interface software and technology
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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.