A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The development of the HTK Broadcast News transcription system: an overview
Speech Communication - Special issue on automatic transcription of broadcast news data
Modern Information Retrieval
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Investigation of silicon auditory models and generalization of linear discriminant analysis for improved speech recognition
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
The Journal of Machine Learning Research
A discriminative HMM/N-gram-based retrieval approach for mandarin spoken documents
ACM Transactions on Asian Language Information Processing (TALIP)
Meta-evaluation of summaries in a cross-lingual environment using content-based metrics
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Automatic summarization of voicemail messages using lexical and prosodic features
ACM Transactions on Speech and Language Processing (TSLP)
Exploring the use of latent topical information for statistical Chinese spoken document retrieval
Pattern Recognition Letters
Extractive summarization using inter- and intra- event relevance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A bottom-up approach to sentence ordering for multi-document summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Maximum likelihood discriminant feature spaces
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Extractive summarization based on event term clustering
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
A skip-chain conditional random field for ranking meeting utterances by importance
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Summarizing speech without text using hidden Markov models
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Machine-made index for technical literature: an experiment
IBM Journal of Research and Development
Automatic text summarization based on word-clusters and ranking algorithms
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Minimum tag error for discriminative training of conditional random fields
Information Sciences: an International Journal
A Comparative Study of Probabilistic Ranking Models for Chinese Spoken Document Summarization
ACM Transactions on Asian Language Information Processing (TALIP)
Is the contextual information relevant in text clustering by compression?
Expert Systems with Applications: An International Journal
Unsupervised topic modeling approaches to decision summarization in spoken meetings
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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The purpose of extractive summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio and then sequence them to form a concise summary. In this paper, we proposed the use of probabilistic latent topical information for extractive summarization of spoken documents. Various kinds of modeling structures and learning approaches were extensively investigated. In addition, the summarization capabilities were verified by comparison with several conventional spoken document summarization models. The experiments were performed on the Chinese broadcast news collected in Taiwan. Noticeable performance gains were obtained. The proposed summarization technique has also been properly integrated into our prototype system for voice retrieval of Mandarin broadcast news via mobile devices.