Abstract
In this work, we analyze the productivity of meetings and predict productivity levels using linguistic and structural features. This task relates to the task of automatic extractive summarization, as we define productivity in terms of the number (or percentage) of sentences from a meeting that are considered summary-worthy. We describe the traits that differentiate productive and unproductive meetings. We additionally explore how meetings begin and end, and why many meetings are slow to get going and last longer than necessary.
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Murray, G. (2014). Learning How Productive and Unproductive Meetings Differ. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_17
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DOI: https://doi.org/10.1007/978-3-319-06483-3_17
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