Detecting Action Items in Multi-party Meetings: Annotation and Initial Experiments

SRI author:


Purver Matthew, Ehlen Patrick, Niekrasz John. “Detecting Action Items in Multi-party Meetings: Annotation and Initial Experiments”in Machine Learning for Multimodal Interaction: Third International Workshop, MLMI 2006, Bethesda, MD, USA, May 1-4, 2006, Revised Selected Papers”, pp. “200-211”, “Springer Berlin Heidelberg”, 2006.


This paper presents the results of initial investigation and experiments into automatic action item detection from transcripts of multi-party human-human meetings. We start from the flat action item annotations of [1], and show that automatic classification performance is limited. We then describe a new hierarchical annotation schema based on the roles utterances play in the action item assignment process, and propose a corresponding approach to automatic detection that promises improved classification accuracy while also enabling the extraction of useful information for summarization and reporting.

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