Merging Multiple Features to Evaluate the Content of Text Summary

Samira Ellouze, Maher Jaoua, Lamia Hadrich Belguith


In this paper, we propose a method that evaluates the content of a text summary using a machine learning approach. This method operates by combining multiple features to build models that predict the PYRAMID scores for new summaries. We have tested several single and "Ensemble Learning" classifiers to build the best model. The evaluation of summarization system is made using the average of the scores of summaries that are built from each system. The results show that our method has achieved good performance in predicting the content score for a summary as well as for a summarization system.

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