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

Jin Yu, Ehud Baruch Reiter, James Ritchie Wallace Hunter, Christopher Stuart Mellish

Research output: Contribution to journalArticle

63 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)25-49
Number of pages24
JournalNatural Language Engineering
Volume13
Issue number1
DOIs
Publication statusPublished - Mar 2007

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Pattern recognition
Gas turbines
time series
Time series
Turbines
Agglomeration
Sensors
pattern recognition
abstraction
aggregation
language
Summary
Natural Language
Gas
Language Generation
Sensor
Prototype
Pattern Recognition

Cite this

Choosing the content of textual summaries of large time-series data sets. / Yu, Jin; Reiter, Ehud Baruch; Hunter, James Ritchie Wallace; Mellish, Christopher Stuart.

In: Natural Language Engineering, Vol. 13, No. 1, 03.2007, p. 25-49.

Research output: Contribution to journalArticle

Yu, Jin ; Reiter, Ehud Baruch ; Hunter, James Ritchie Wallace ; Mellish, Christopher Stuart. / Choosing the content of textual summaries of large time-series data sets. In: Natural Language Engineering. 2007 ; Vol. 13, No. 1. pp. 25-49.
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