Automatic Arabic Text Summarization Using Analogical Proportions
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Automatic Arabic Text Summarization Using Analogical Proportions Bilel Elayeb1,2 · Amina Chouigui2 · Myriam Bounhas1,3 · Oussama Ben Khiroun2,4 Received: 29 November 2019 / Accepted: 10 June 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Automatic text summarization is the process of generating or extracting a brief representation of an input text. There are several algorithms for extractive summarization in the literature tested by using English and other languages datasets; however, only few extractive Arabic summarizers exist due to the lack of large collection in Arabic language. This paper proposes and assesses new extractive single-document summarization approaches based on analogical proportions which are statements of the form “a is to b as c is to d”. The goal is to study the capability of analogical proportions to represent the relationship between documents and their corresponding summaries. For this purpose, we suggest two algorithms to quantify the relevance/irrelevance of an extracted keyword from the input text, to build its summary. In the first algorithm, the analogical proportion representing this relationship is limited to check the existence/non-existence of the keyword in any document or summary in a binary way without considering keyword frequency in the text, whereas the analogical proportion of the second algorithm considers this frequency. We have assessed and compared these two algorithms with some languageindependent summarizers (LexRank, TextRank, Luhn and LSA (Latent Semantic Analysis)) using our large corpus ANT (Arabic News Texts) and a small test collection EASC (Essex Arabic Summaries Corpus) by computing ROUGE (RecallOriented Understudy for Gisting Evaluation) and BLEU (BiLingual Evaluation Understudy) metrics. The best-achieved results are ROUGE-1 = 0.96 and BLEU-1 = 0.65 corresponding to educational documents from EASC collection which outperform the best LexRank algorithm. The proposed algorithms are also compared with three other Arabic extractive summarizers, using EASC collection, and show better results in terms of ROUGE-1 = 0.75 and BLEU-1 = 0.47 for the first algorithm, and ROUGE-1 = 0.74 and BLEU-1 = 0.49 for the second one. Experimental results show the interest of analogical proportions for text summarization. In particular, analogical summarizers significantly outperform three among four language-independent summarizers in the case of BLEU-1 for ANT collection and they are not significantly outperformed by any other summarizer in the case of EASC collection. Keywords Arabic text summarization · Extractive summarization · Analogical proportions · Analogical relevance
Introduction Automatic text summarization is an important research task in the field of natural language processing (NLP). Text summarization aims to automatically produce a brief summary, with few words, that represents the input text content. The output summary should maintain the document’s meaning by selecting the relevant information and keeping the sentences’ cohe
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