Summarizing non-textual events with a 'briefing' focus

  • Authors:
  • Mohit Kumar;Dipanjan Das;Alexander I. Rudnicky

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
  • Year:
  • 2007

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Abstract

We describe a learning-based system for generating reports based on a mix of text and event data. The system incorporates several stages of processing, including aggregation, template-filling and importance ranking. Aggregators and templates were based on a corpus of reports evaluated by human judges. Importance and granularity were learned from this corpus as well. We find that high-scoring reports (with a recall of 0.89) can be reliably produced using this procedure given a set of oracle features. The report drafting system is part of a learning cognitive assistant RADAR, and is used to describe its performance.