Combinación varias Características para evaluar el contenido del resumen de texto

In this paper, we propose a method that evaluates the content of a text summary using a machine learning approach. This method operates by combining multiple features to build models that predict the PYRAMID scores for new summaries. We have tested several single and "Ensemble Learning" classifiers to build the best model. The evaluation of summarization system is made using the average of the scores of summaries that are built from each system. The results show that our method has achieved good performance in predicting the content score for a summary as well as for a summarization system.En este artículo proponemos un método que evalúa el contenido de un resumen de texto utilizando un enfoque de aprendizaje automático. Este método funciona combinando múltiples Características para construir modelos que predicen las puntuaciones PYRAMID para nuevos resúmenes. Hemos probado varios clasificadores individuales y "Ensemble Learning" para construir el mejor modelo. La evaluación del sistema de resumen se realiza utilizando el promedio de las puntuaciones de los resúmenes que se construyen a partir de cada sistema. Los resultados muestran que nuestro método ha logrado un buen rendimiento en la predicción de la puntuación de contenido para un resumen, así como para un sistema de resumen

Mix Multiple Features to Evaluate the Content and the Linguistic Quality of Text Summaries

In this article, we propose a method of text summary\u27s 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

A novel knowledge-discovering approach from massive data

Poster at the 7th International Conference on Data Mining (DMIN 2011), Las Vegas, Nevada, US

Orthographic Transcription for Spoken Tunisian Arabic

International audienc

Automatic summarization of Semitic languages

This chapter addresses automatic summarization of Semitic languages. After a presentation of the theoretical background and current challenges of the automatic summarization, we present different approaches suggested to cope with these challenges. These approaches fall on to two classes: single vs. multiple document summarization approaches. The main approaches dealing with Semitic languages (mainly Arabic, Hebrew, Maltese and Amharic) are then discussed. Finally, a case study of a specific Arabic automatic summarization system is presented. The summary section draws the most insightful conclusions and discusses some future research direction