Non-textual event summarization by applying machine learning to template-based language generation

Authors:
Mohit Kumar;Dipanjan Das;Sachin Agarwal;Alexander I. Rudnicky
Affiliations:
Carnegie Mellon University, Pittsburgh;Carnegie Mellon University, Pittsburgh;Carnegie Mellon University, Pittsburgh;Carnegie Mellon University, Pittsburgh
Venue:
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Year:
2009

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Abstract

We describe a learning-based system that creates draft reports based on observation of people preparing such reports in a target domain (conference replanning). The reports (or briefings) are based on a mix of text and event data. The latter consist of task creation and completion actions, collected from a wide variety of sources within the target environment. The report drafting system is part of a larger learning-based cognitive assistant system that improves the quality of its assistance based on an opportunity to learn from observation. The system can learn to accurately predict the briefing assembly behavior and shows significant performance improvements relative to a non-learning system, demonstrating that it's possible to create meaningful verbal descriptions of activity from event streams.