Making large-scale support vector machine learning practical
Advances in kernel methods
Automatic summarization of open-domain multiparty dialogues in diverse genres
Computational Linguistics - Summarization
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Gemini: a natural language system for spoken-language understanding
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
First Steps Towards the Automatic Construction of Argument-Diagrams from Real Discussions
Proceedings of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006
Modelling and detecting decisions in multi-party dialogue
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Analysing meeting records: an ethnographic study and technological implications
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Decision detection using hierarchical graphical models
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Summarizing decisions in spoken meetings
WASDGML '11 Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages
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
Focused meeting summarization via unsupervised relation extraction
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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We use directed graphical models (DGMs) to automatically detect decision discussions in multi-party dialogue. Our approach distinguishes between different dialogue act (DA) types based on their role in the formulation of a decision. DGMs enable us to model dependencies, including sequential ones. We summarize decisions by extracting suitable phrases from DAs that concern the issue under discussion and its resolution. Here we use a semantic-similarity metric to improve results on both manual and ASR transcripts.