Meeting Analysis: Findings from Research and Practice
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 1 - Volume 1
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Detection of agreement vs. disagreement in meetings: training with unlabeled data
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Prosodic correlates of rhetorical relations
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
You are what you say: using meeting participants' speech to detect their roles and expertise
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
Shallow discourse structure for action item detection
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
The AMI meeting corpus: a pre-announcement
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
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
Detecting Action Items in Meetings
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Modelling and detecting decisions in multi-party dialogue
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Classification of patient case discussions through analysis of vocalisation graphs
Proceedings of the 2009 international conference on Multimodal interfaces
Real-time decision detection in multi-party dialogue
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Interpretation and transformation for abstracting conversations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Decision detection using hierarchical graphical models
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Abstractive summarization of voice communications
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
Visual structured summaries of human conversations
Proceedings of the first international workshop on Intelligent visual interfaces for text analysis
Summarizing decisions in spoken meetings
WASDGML '11 Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages
Proceedings of the 15th ACM on International conference on multimodal interaction
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Decision making is an important aspect of meetings in organisational settings, and archives of meeting recordings constitute a valuable source of information about the decisions made. However, standard utilities such as playback and keyword search are not sufficient for locating decision points from meeting archives. In this paper, we present the AMI DecisionDetector, a system that automatically detects and highlights where the decision-related conversations are. In this paper, we apply the models developed in our previous work [1], which detects decision-related dialogue acts (DAs) from parts of the transcripts that have been manually annotated as extract-worthy, to the task of detecting decision-related DAs and topic segments directly from complete transcripts. Results show that we need to combine features extracted from multiple knowledge sources (e.g., lexical, prosodic, DA-related, and topical class) in order to yield the model with the highest precision. We have provided a quantitative account of the feature class effects. As our ultimate goal is to operate AMI DecisionDetector in a fully automatic fashion, we also investigate the impacts of using automatically generated features, for example, the 5-class DA features obtained in [2].