Samira Ellouze

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This paper presents a new automated method for evaluating the content of a text summary. The proposed method is based on a combination of features encompassing scores of content and others of linguistic quality. This method relies on a learning technique called linear regression. The objective of this combination is to predict the PYRAMID score from the(More)
The present paper introduces a newMultiling 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(More)
In this brief report we present an overview of the MultiLing 2017 effort and workshop, as implemented within EACL 2017. MultiLing is a community-driven initiative that pushes the state-of-the-art in Automatic Summarization by providing data sets and fostering further research and development of summarization systems. This year the scope of the workshop was(More)
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(More)
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