Abstract
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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Purver, M., Ehlen, P., Niekrasz, J. (2006). Detecting Action Items in Multi-party Meetings: Annotation and Initial Experiments. In: Renals, S., Bengio, S., Fiscus, J.G. (eds) Machine Learning for Multimodal Interaction. MLMI 2006. Lecture Notes in Computer Science, vol 4299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11965152_18
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DOI: https://doi.org/10.1007/11965152_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69267-6
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