The explosion of online and offline data has changed how we gather, evaluate, understand data. It is frequently difficult time-consuming to comprehend large text documents extract crucial information from them. Text summarization techniques address the mentioned problems by compressing long texts while retaining their essential contents. These rely on fast delivery filtered, high-quality conten...

Deep Learning models based on the Transformer architecture have revolutionized state of art NLP tasks. As English is language in which most significant advances are made, languages like Spanish require specific training, but this training has a computational cost so high that only big corporations with servers and GPUs capable generating them. This work explored how to create model for from mul...

Abstract. The increase of information available in the form of text, led to the need of extensive research in the area of text summarization. Early the researches in this area started with single document summarization and drove towards multi document summarization. We present here a comparative review of the recent progress in the field of multi document summarization. The strengths and weakne...

In this paper we address the automatic summarization task. Recent research works on extractive-summary generation employ some heuristics, but few works indicate how to select the relevant features. We will present a summarization procedure based on the application of trainable Machine Learning algorithms which employs a set of features extracted directly from the original text. These features a...

Text classification (TC) or text categorization task is assigning a document to one or more predefined classes or categories. A common problem in TC is the high number of terms or features in document(s) to be classified (the curse of dimensionality). This problem can be solved by selecting the most important terms. In this study, an automatic text summarization is used for feature selection. S...

This Ph.D. thesis is the result of several years of research on automatic text summarization. Three major contributions are presented in the form of published and yet to be published papers. They follow a path that moves away from extractive summarization and toward abstractive summarization. The first article describes the HexTac experiment, which was conducted to evaluate the performance of h...

For the blessing of World Wide Web, the corpus of online information is gigantic in its volume. Search engines have been developed such as Google, AltaVista, Yahoo, etc., to retrieve specific information from this huge amount of data. But the outcome of search engine is unable to provide expected result as the quantity of information is increasing enormously day by day and the findings are abun...