@inproceedings{ellouze-etal-2017-machine,
title = "Machine Learning Approach to Evaluate {M}ulti{L}ingual Summaries",
author = "Ellouze, Samira and
Jaoua, Maher and
Hadrich Belguith, Lamia",
editor = "Giannakopoulos, George and
Lloret, Elena and
Conroy, John M. and
Steinberger, Josef and
Litvak, Marina and
Rankel, Peter and
Favre, Benoit",
booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1007",
doi = "10.18653/v1/W17-1007",
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.",
}
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%0 Conference Proceedings
%T Machine Learning Approach to Evaluate MultiLingual Summaries
%A Ellouze, Samira
%A Jaoua, Maher
%A Hadrich Belguith, Lamia
%Y Giannakopoulos, George
%Y Lloret, Elena
%Y Conroy, John M.
%Y Steinberger, Josef
%Y Litvak, Marina
%Y Rankel, Peter
%Y Favre, Benoit
%S Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F ellouze-etal-2017-machine
%X 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.
%R 10.18653/v1/W17-1007
%U https://aclanthology.org/W17-1007
%U https://doi.org/10.18653/v1/W17-1007
%P 47-54
Markdown (Informal)
[Machine Learning Approach to Evaluate MultiLingual Summaries](https://aclanthology.org/W17-1007) (Ellouze et al., MultiLing 2017)
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.