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 journalArticlepeer-review

74 Citations (Scopus)


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
Issue number1
Publication statusPublished - Mar 2007


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