@InProceedings{ellouze-jaoua-hadrichbelguith:2017:MultiLing2017,
  author    = {Ellouze, Samira  and  Jaoua, Maher  and  Hadrich Belguith, Lamia},
  title     = {Machine Learning Approach to Evaluate MultiLingual Summaries},
  booktitle = {Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {47--54},
  abstract  = {The present paper introduces a new MultiLing text summary evaluation method.
	This method relies on machine learning approach which operates by combining
	multiple features to build models that predict the human score (overall
	responsiveness) of a new summary. We have tried several single and ``ensemble
	learning'' classifiers to build the best model. We have experimented our
	method
	in summary level evaluation where we evaluate each text summary separately. The
	correlation between built models and human score is better than the correlation
	between baselines and manual score.},
  url       = {http://www.aclweb.org/anthology/W17-1007}
}