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Choosing the content of textual summaries of large time-series data sets

Published online by Cambridge University Press:  15 February 2006

JIN YU
Affiliation:
Department of Computing Science, University of Aberdeen Aberdeen AB24 3UE, UK e-mail: jyu@csd.abdn.ac.uk, ereiter@csd.abdn.ac.uk, jhunter@csd.abdn.ac.uk, cmellish@csd.abdn.ac.uk
EHUD REITER
Affiliation:
Department of Computing Science, University of Aberdeen Aberdeen AB24 3UE, UK e-mail: jyu@csd.abdn.ac.uk, ereiter@csd.abdn.ac.uk, jhunter@csd.abdn.ac.uk, cmellish@csd.abdn.ac.uk
JIM HUNTER
Affiliation:
Department of Computing Science, University of Aberdeen Aberdeen AB24 3UE, UK e-mail: jyu@csd.abdn.ac.uk, ereiter@csd.abdn.ac.uk, jhunter@csd.abdn.ac.uk, cmellish@csd.abdn.ac.uk
CHRIS MELLISH
Affiliation:
Department of Computing Science, University of Aberdeen Aberdeen AB24 3UE, UK e-mail: jyu@csd.abdn.ac.uk, ereiter@csd.abdn.ac.uk, jhunter@csd.abdn.ac.uk, cmellish@csd.abdn.ac.uk

Abstract

Natural Language Generation (NLG) can be used to generate textual summaries of numeric data sets. In this paper we develop an architecture for generating short (a few sentences) summaries of large (100KB or more) time-series data sets. The architecture integrates pattern recognition, pattern abstraction, selection of the most significant patterns, microplanning (especially aggregation), and realisation. We also describe and evaluate SumTime-Turbine, a prototype system which uses this architecture to generate textualsummaries of sensor data from gas turbines.

Type
Papers
Copyright
2006 Cambridge University Press

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