Original scientific paper
https://doi.org/10.20532/cit.2017.1003398
Mix Multiple Features to Evaluate the Content and the Linguistic Quality of Text Summaries
Samira Ellouze
; University of Sfax, Faculty of Economics and Management of Sfax, Sfax, Tunisia
Maher Jaoua
; University of Sfax, Faculty of Economics and Management of Sfax, Sfax, Tunisia
Lamia Hadrich Belguith
; University of Sfax, Faculty of Economics and Management of Sfax, Sfax, Tunisia
Abstract
In this article, we propose a method of text summary's content and linguistic quality evaluation that is based on a machine learning approach. This method operates by combining multiple features to build predictive models that evaluate the content and the linguistic quality of new summaries (unseen) constructed from the same source documents as the summaries used in the training and the validation of models. To obtain the best model, many single and ensemble learning classifiers are tested. Using the constructed models, we have achieved a good performance in predicting the content and the linguistic quality scores. In order to evaluate the summarization systems, we calculated the system score as the average of the score of summaries that are built from the same system. Then, we evaluated the correlation of the system score with the manual system score. The obtained correlation indicates that the system score outperforms the baseline scores.
Keywords
text summary; summary evaluation; content; linguistic quality; machine learning
Hrčak ID:
183330
URI
Publication date:
26.6.2017.
Visits: 1.105 *