A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian query-focused summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Discriminative n-gram language modeling
Computer Speech and Language
Soft indexing of speech content for search in spoken documents
Computer Speech and Language
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-document summarization using cluster-based link analysis
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Word Topic Models for Spoken Document Retrieval and Transcription
ACM Transactions on Asian Language Information Processing (TALIP)
A Comparative Study of Probabilistic Ranking Models for Chinese Spoken Document Summarization
ACM Transactions on Asian Language Information Processing (TALIP)
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Document summarization using conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Machine-made index for technical literature: an experiment
IBM Journal of Research and Development
Improving supervised learning for meeting summarization using sampling and regression
Computer Speech and Language
Exploring correlation between ROUGE and human evaluation on meeting summaries
IEEE Transactions on Audio, Speech, and Language Processing
Applying regression models to query-focused multi-document summarization
Information Processing and Management: an International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Extractive chinese spoken document summarization using probabilistic ranking models
ISCSLP'06 Proceedings of the 5th international conference on Chinese Spoken Language Processing
Leveraging Kullback–Leibler Divergence Measures and Information-Rich Cues for Speech Summarization
IEEE Transactions on Audio, Speech, and Language Processing
A Probabilistic Generative Framework for Extractive Broadcast News Speech Summarization
IEEE Transactions on Audio, Speech, and Language Processing
A Cascaded Broadcast News Highlighter
IEEE Transactions on Audio, Speech, and Language Processing
Leveraging relevance cues for language modeling in speech recognition
Information Processing and Management: an International Journal
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The purpose of extractive speech summarization is to automatically select a number of indicative sentences or paragraphs (or audio segments) from the original spoken document according to a target summarization ratio and then concatenate them to form a concise summary. Much work on extractive summarization has been initiated for developing machine-learning approaches that usually cast important sentence selection as a two-class classification problem and have been applied with some success to a number of speech summarization tasks. However, the imbalanced-data problem sometimes results in a trained speech summarizer with unsatisfactory performance. Furthermore, training the summarizer by improving the associated classification accuracy does not always lead to better summarization evaluation performance. In view of such phenomena, we present in this paper an empirical investigation of the merits of two schools of training criteria to alleviate the negative effects caused by the aforementioned problems, as well as to boost the summarization performance. One is to learn the classification capability of a summarizer on the basis of the pair-wise ordering information of sentences in a training document according to a degree of importance. The other is to train the summarizer by directly maximizing the associated evaluation score or optimizing an objective that is linked to the ultimate evaluation. Experimental results on the broadcast news summarization task suggest that these training criteria can give substantial improvements over a few existing summarization methods.