Machine Learning Approach to Evaluate MultiLingual Summaries

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" classiers to build the best model. We have experimented our method in summary level evaluation where we evaluate the quality of each text summary separately. The correlation between built models and human score is better than the correlation between the baselines and the manual score.