Text mining based on analogical proportions: Application to Arabic text summarization andclassification

A huge number of textual documents regularly enrich the World Wide Web. This big amount of data, produced by various sources such as news websites, is among the main sources of documents on the Internet and Web 2.0. However, the extraction of useful information from these unstructured data becomes a hard task requiring a complex step of data processing and management. Given the huge amount of textual data in the Web, there is a serious need for text mining applications such as text summarization and classification.This thesis studies the ability of Analogical proportions as a promising tool for text summarization and classification. Analogical proportions (AP) are statements of the form “a is to b as c is to d” usually denoted a : b :: c : d and expressing that “a differs from b as c differs from d”, as well as “b differs from a as d differs from c”. Analogical inference is based on the assumption that if four items a, b, c, d are making a valid analogical proportion and if d is unknown, this enables us to predict the value of d on the basis of the values of the triplet (a, b, c). Based on the above principle, analogical proportions have recently proven their efficiency to classify “structured” datasets.Our main interest in this work is to validate their efficiency to deal with “unstructured” datasets. We first propose in this thesis a new free structured Arabic dataset in a standard XML TREC format, named ANT corpus (https://antcorpus.github.io/). This latter is collected using RSS feeds from various news websites. It is used for many text-mining tasks, such as Arabic text summarization and classification. The ANT collection contains documents from five various news websites that are classified into six categories. This resource is available online in different versions. We exploit the ANT corpus v2.1 to treat two different problems: i) extractive text summarization and ii) text classification.The goal of automatic text summarization is to represent the main text by an extracted set of sentences. Various types of summarizers that are either extractive, abstractive or hybrid algorithms are available in the literature, such as TextRank, LexRank, LSA and Luhn. In this current work, we focus on unsupervised single- document extractive summarization.For this purpose, we first investigate the ability of analogical proportions to represent the relationship between documents and their corresponding summaries. Then, we suggest two various procedures to quantify the relevance/irrelevance of a given term (keyword), issued from the original text, to build its summary. In the first model, the analogical proportion, which represents this relationship, considers only the existence/non- existence of the keyword in the main text or summary in a binary way without any interest in the keyword frequency of the input text. In the second model, the keyword frequency is quantified in the analogical proportion. Then, we develop two algorithms respectively called AATS1 and AATS2 that implement respectively the first and second models. To assess the efficiency of the suggested algorithms, we exploit two datasets: ANT corpus v2.1 and EASC (https://sourceforge.net/projects/easc-corpus/). Inboth collections, the input document and its reference summary are available. To evaluate the proposed summarizers, we compute the ROUGE and BLEU metrics that compare the extracted summaries, from each category to the referencesummaries. The achieved results show the interest of analogical proportions for text summarization if compared to language-independent summarizers or Arabic summarizers, using large (ANT corpus) and small (EASC) corpora. The proposed summarizers outperform the language-independent summarizers in terms of BLEU-1 and ROUGE-1 scores for the "Education" category from the EASC dataset. On the other hand, our AATS algorithms perform better than the three Arabic single document extractive summarizers (the score-based summarizer (2021), ASDKGA (2018) and LCEAS(2015)), tested on the EASC dataset, in terms of ROUGE-1 score. In particular, the AATS algorithms significantly outperform three among four language- independent summarizers (LexRank, TextRank, Luhn and LSA) in case of BLEU-1 for the ANT corpus, and they are not significantly outperformed by any other summarizer in the case of theEASC corpus.We also use the ANT corpus v2.1 for automatic text classification. The process of classifying or categorizing texts consists to assign a predefined class label to an unlabeled text based on its content. The classification model efficiency is sensitive to the high dimensionality of data that can affect the classifier accuracy. Many fields of application can benefit from text classification achievements, in particular recommendation systems, search engines and NLP tools.Great research effort has been devoted to studying the classification problems of English texts.