An Extractive-Summarization Baseline for the Automatic Detection of Noteworthy Utterances in Multi-Party Human-Human Dialog

Our goal is to reduce meeting participants’ note-taking effort by  automatically identifying utterances whose contents meeting participants are likely to include in their notes. Though  note-taking is different from meeting summarization, these two problems are related. In this paper we  apply techniques developed in extractive meeting summarization  research to the problem of identifying noteworthy utterances. We show that these algorithms achieve an f-measure of 0.14 over a 5-meeting sequence of related meetings. The precision – 0.15  – is triple that of the trivial baseline of simply labeling every utterance as noteworthy.  We  also introduce the concept of “show-worthy” utterances –utterances that  contain  information that could conceivably  result in a note.  We show that such utterances can be recognized with  an  81% accuracy (compared to 53%  accuracy of a majority classifier). Further, if non-show-worthy utterances are filtered out,  the  precision of  noteworthiness detection improves  by 33% relative.