Automatically detecting action items in audio meeting recordings

Identification of action items in meeting recordings can provide immediate access to salient information in a medium notoriously difficult to search and summarize. To this end, we use a maximum entropy model to automatically detect action item-related utterances from multi-party audio meeting recordings. We compare the effect of lexical, temporal, syntactic, semantic, and prosodic features on system performance. We show that on a corpus of action item annotations on the ICSI meeting recordings, characterized by high imbalance and low inter-annotator agreement, the system performs at an F measure of 31.92%. While this is low compared to better-studied tasks on more mature corpora, the relative usefulness of the features towards this task is indicative of their usefulness on more consistent annotations, as well as to related tasks.