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- W1963586186 abstract "Our goal is to make note-taking easier meetings by automatically detecting noteworthy utterances verbal exchanges and suggesting them to meeting participants for inclusion their notes. To show feasibility of such a process we conducted a Wizard of Oz study where the Wizard picked automatically transcribed utterances that he judged as noteworthy, and suggested their contents to the participants as notes. Over 9 meetings, participants accepted 35% of these suggestions. Further, 41.5% of their notes at the end of the meeting contained Wizard-suggested text. Next, order to perform noteworthiness detection automatically, we annotated a set of 6 meetings with a 3-level noteworthiness annotation scheme, which is a break from the binary in summary/not summary labeling typically used speech summarization. We report Kappa of 0.44 for the 3-way classification, and 0.58 when two of the 3 labels are merged into one. Finally, we trained an SVM classifier on this annotated data; this classifier's performance lies between that of trivial baselines and inter-annotator agreement." @default.
- W1963586186 created "2016-06-24" @default.
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- W1963586186 date "2009-01-01" @default.
- W1963586186 modified "2023-09-22" @default.
- W1963586186 title "Detecting the noteworthiness of utterances in human meetings" @default.
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- W1963586186 doi "https://doi.org/10.3115/1708376.1708386" @default.
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