Automated summaries help tackle the ever growing volume of information floating around.
There are two broad categories: extract and abstract. In the former we retain the more …

In the context of Information Retrieval, Arabic stemming algorithms have become a most
research area of information retrieval. Many researchers have developed algorithms to solve …

Morphological analyzers are preprocessors for text analysis. Many Text Analytics
applications need them to perform their tasks. The aim of this thesis is to develop standards …

Automatic text summarization is a field situated at the intersection of natural language
processing and information retrieval. Its main objective is to automatically produce a …

Topic segmentation is important for many natural language processing applications such as
information retrieval, text summarization. In our work, we are interested in the topic …

Algorithms for Arabic stemming available in two main types which are rootbased approach
and stem-based approach. Both types have problems which have been solved in the …

Text summarisation is one of the interesting tools for a quick and optimal exploitation of the
huge amount of online textual documents. Several approaches have been proposed to date …

Topic Segmentation is one of the pillars of Natural Language Processing. Yet there is a
remarkable research gap in this field, as far as the Arabic language is concerned. The …

As the number of electronic text documents is increasing so is the need for an automatic text
summarizer. The summary can be extractive, compression, or abstractive. In the former, the …

This research presents a methodology for trusting the provenance of data on the web. The
implication is that data does not change after publication and the source of the data is stable …