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
Hedge Trimmer: a parse-and-trim approach to headline generation
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
Improving extractive dialogue summarization by utilizing human feedback
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Extrinsic summarization evaluation: A decision audit task
ACM Transactions on Speech and Language Processing (TSLP)
A sentence compression module for machine-assisted subtitling
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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This paper proposes an automatic speech summarization technique for English. In our proposed method, a set of words maximizing a summarization score indicating appropriateness of summarization is extracted from automatically transcribed speech and concatenated to create a summary. The extraction process is performed using a Dynamic Programming (DP) technique according to a target compression ratio. In this paper, English broadcast news speech transcribed using a speech recognizer is automatically summarized. In order to apply our method, originally proposed for Japanese, to English, the model of estimating word concatenation probabilities based on a dependency structure in the original speech given by a Stochastic Dependency Context Free Grammar (SDCFG) is modified. A summarization method for multiple utterances using two-level DP technique is also proposed. The automatically summarized sentences are evaluated by a summarization accuracy based on the comparison with the manual summarization of correctly transcribed speech by human subjects. Experimental results show that our proposed method effectively extracts relatively important information and remove redundant and irrelevant information from English news speech.