Detecting Summarization Hot Spots In Meetings Using Group Level Involvement And Turn-Taking Features

In this paper we investigate how participant involvement and turn-taking features relate to extractive summarization of meeting dialogues. In particular, we examine whether automatically derived measures of group level involvement, like participation equality and turn -taking freedom, can help detect where summarization relevant meeting segments will be. Results show that classification using turn -taking features performed better than the majority class baseline for data from both AMI and ICSI meeting corpora in identifying whether meeting segments contain extractive summary dialogue acts. The feature based approach also provided better recall than using manual ICSI involvement hot spot annotations. Turn -taking features were additionally found to be predictive of the amount of extractive summary content in a segment. In general, we find that summary content decreases with higher participation equality and overlap, while it increases with the number of very short utterances. Differences in results between the AMU and ICSI data sets suggest how group participatory structure can be used to understand what makes meetings easy or difficult to summarize.