Sciweavers

NLE
2007

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

13 years 10 months ago
Choosing the content of textual summaries of large time-series data sets
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, 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 textual summaries of sensor data from gas turbines.
Jin Yu, Ehud Reiter, Jim Hunter, Chris Mellish
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where NLE
Authors Jin Yu, Ehud Reiter, Jim Hunter, Chris Mellish
Comments (0)