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
Automatic summarization of open-domain multiparty dialogues in diverse genres
Computational Linguistics - Summarization
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Extractive spoken document summarization for information retrieval
Pattern Recognition Letters
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
A global optimization framework for meeting summarization
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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
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NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Unsupervised approaches for automatic keyword extraction using meeting transcripts
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A risk minimization framework for extractive speech summarization
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A hybrid hierarchical model for multi-document summarization
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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INLG '10 Proceedings of the 6th International Natural Language Generation Conference
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WASDGML '11 Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages
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MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Multi-aspect Sentiment Analysis with Topic Models
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
A Supervised Framework for Keyword Extraction From Meeting Transcripts
IEEE Transactions on Audio, Speech, and Language Processing
Methods for Mining and Summarizing Text Conversations
Methods for Mining and Summarizing Text Conversations
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We present a token-level decision summarization framework that utilizes the latent topic structures of utterances to identify "summary-worthy" words. Concretely, a series of unsupervised topic models is explored and experimental results show that fine-grained topic models, which discover topics at the utterance-level rather than the document-level, can better identify the gist of the decision-making process. Moreover, our proposed token-level summarization approach, which is able to remove redundancies within utterances, outperforms existing utterance ranking based summarization methods. Finally, context information is also investigated to add additional relevant information to the summary.