Automatic summarization of open-domain multiparty dialogues in diverse genres
Computational Linguistics - Summarization
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
Summarizing multilingual spoken negotiation dialogues
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Automatic summarization of voicemail messages using lexical and prosodic features
ACM Transactions on Speech and Language Processing (TSLP)
Incorporating speaker and discourse features into speech summarization
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
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
Term-weighting for summarization of multi-party spoken dialogues
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
The AMI meeting corpus: a pre-announcement
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
A Cascaded Broadcast News Highlighter
IEEE Transactions on Audio, Speech, and Language Processing
Participants' personal note-taking in meetings and its value for automatic meeting summarisation
Information Technology and Management
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This paper is about the extractive summarization of meeting speech, using the ICSI and AMI corpora. In the first set of experiments we use prosodic, lexical, structural and speaker-related features to select the most informative dialogue acts from each meeting, with the hypothesis being that such a rich mixture of features will yield the best results. In the second part, we present an approach in which the identification of "meta-comments" is used to create more informative summaries that provide an increased level of abstraction. We find that the inclusion of these meta comments improves summarization performance according to several evaluation metrics.