Machine Learning Approach to Evaluate MultiLingual Summaries

Samira Ellouze, Maher Jaoua, Lamia Hadrich Belguith


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.
Anthology ID:
W17-1007
Volume:
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
George Giannakopoulos, Elena Lloret, John M. Conroy, Josef Steinberger, Marina Litvak, Peter Rankel, Benoit Favre
Venue:
MultiLing
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
47–54
Language:
URL:
https://aclanthology.org/W17-1007
DOI:
10.18653/v1/W17-1007
Bibkey:
Cite (ACL):
Samira Ellouze, Maher Jaoua, and Lamia Hadrich Belguith. 2017. Machine Learning Approach to Evaluate MultiLingual Summaries. In Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres, pages 47–54, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Machine Learning Approach to Evaluate MultiLingual Summaries (Ellouze et al., MultiLing 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/W17-1007.pdf