Analogical Text Mining: Application to Arabic Text Summarization and Classification

This paper 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, especially for Arabic text summarization and classification.