![summarize machine summarize machine](https://i.ytimg.com/vi/2d87LG9gmW8/hqdefault.jpg)
130–137 (2009)ĭuboue, P.A., McKeown, K.R.: Statistical Acquisition of Content Selection Rules for Natural Language Generation (EMNLP 2003), pp. In: Proceedings of the 12th European Workshop on Natural Language Generation (ENLG 2009), Athens, pp. Kelly, C., Copestake, A., Karamanis, N.: Investigating content selection for language generation using machine learning. In: Proceedings of the 3rd Corpus Linguistics Conference (CL 2005) (2005)
![summarize machine summarize machine](https://www.qlqcompany.com/uploads/allimg/181104/8-1Q1041H343.jpg)
This process is experimental and the keywords may be updated as the learning algorithm improves.īelz, A.: Corpus-driven generation of weather forecasts. These keywords were added by machine and not by the authors. We evaluate our techniques on a parallel corpus of human generated weather forecast text corresponding to numerical weather prediction data.
#SUMMARIZE MACHINE SERIES#
We present an approach to select important points in a time series that can aid in generating captions or textual summaries. Machine learning approaches are used to induce the underlying rules for text summarization, which are potentially close to the ones that humans use to generate textual summaries. This is an important step towards building an automated NLG (Natural Language Generation) system to generate text for unseen data. The goal is to exploit a parallel corpus to predict the appropriate level of abstraction required for a summarization task. In this paper we focus on content selection for summarizing time series data using Machine Learning techniques.