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Automated Detection of Action Items in Audio Meeting Records

This study focuses on detecting action items in multi-party audio meeting recordings using a maximum entropy model trained on various features. Results indicate the importance of temporal, contextual, and fine dialog act features. The combination of features achieved a 31.92 F-score, with a focus on the top 15 meetings ranked by kappa. The study highlights the usefulness of lexical, syntactic, prosodic, semantic, and temporal features in identifying decision points that require post-meeting attention.

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Automated Detection of Action Items in Audio Meeting Records

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  1. Automatically Detecting Action Items in Audio Meeting Records William Morgan, Pi-Chuan Chang, Surabhi Gupta, Jason M. Brenier Natural Language Processing Group Department of Computer Science Stanford University, USA Summary Results • TASK: Detection of action items (decisions made during a meeting that require post meeting attention) from multi-party audio meeting recordings. • APPROACH: Maximum entropy model trained on lexical, syntactic, temporal, semantic and prosodic features. • RESULTS: Combination of features described below achieves 31.92 F score Data • ICSI Meeting Corpus with annotations for action items (Gruenstein et al. 2005) • Inter-annotator agreement with 2 annotators: 0.364 kappa score • Imbalanced data: 590 action item utterances in 24,250 total utterances (2.4%) • We focus on top 15 meetings as ranked by kappa. Min kappa = 0.435 • Gold Standard for results: union of action items from both annotators Analysis Formulation unigram+bigram Best-performing unigram • Binary classification task on a per utterance level • Each utterance is either yes/ no for action item. • Imbalanced binary classification • F measure is more suitable for evaluation than accuracy. • If every utterance is marked false, accuracy = 97.5% and F score = 0 • If we were to use a weighted coin in proportion to the number of positive examples, accuracy = 95.25% and P = R = F = 2.4%. • Maximum entropy model Features • Immediate lexical features -- word unigrams and bigrams • Contextual lexical features -- lexical features for neighboring utterances • Syntactic features -- POS tags (UH, MD, NN*, VB*, VBD) • Prosodic features -- intensity, pitch, duration • Temporal features -- duration, time of occurrence until the end of the meeting • General semantic features -- temporal expressions from Identifinder (e.g. next Tuesday) • Dialog-specific semantic -- Dialog acts (56 fine DA’s e.g. rhetorical features question, 7 coarse DA’s e.g. statement, question) Conclusion • Temporal, contextual and fine DA features were found to be most useful. • Raw system performance numbers are low. But relative usefulness of features towards this task is indicative of their usefulness in more mature corpora and related tasks. • Thanks to Dan Jurafsky, Chris Manning, Stanley Peters, Matthew Purver and all the anonymous reviewers.

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