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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.

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Automatically Detecting Action Items in Audio Meeting Records

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Automatically detecting action items in audio meeting records

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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