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Summarizing Non-textual Events with a 'Briefing' Focus
Recherche d'Information Assistée par Ordinateur (RIAO), Pittsburgh
  • Mohit Kumar, Carnegie Mellon University
  • Dipanjan Das, Carnegie Mellon University
  • Alexander I Rudnicky, Carnegie Mellon University
Date of Original Version
Conference Proceeding
Abstract or Description
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.
Citation Information
Mohit Kumar, Dipanjan Das and Alexander I Rudnicky. "Summarizing Non-textual Events with a 'Briefing' Focus" Recherche d'Information Assistée par Ordinateur (RIAO), Pittsburgh (2007)
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