Automatic Arabic Text Summarization Using Analogical Proportions

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 (Recall-Oriented 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.

A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments

“…We also aim to adapt the system for other languages and applications, e.g. automated Arabic text summarization [63,64]. Another area of interest is studying aspects of Arabic hadith literature.…”

A Survey of Automatic Text Summarization: Progress, Process and Challenges

“…ROUGE consists of five measures: ROUGE-N, ROUGE-L, ROUGE-W, ROUGE-S, and ROUGE-SU. The examples where the ROUGH metrics were used can be found in [146], [338], [339] and [340].…”

Recent Advances in Arabic Automatic Text Summarization

“…Elayeb et al [11] presented an extractive approach for Arabic single-document summarization using analogical proportions by representing the relationship between documents and their corresponding summaries. Two algorithms are implemented, the first one checks for the existence of the keywords in the document or in the summary while the second algorithm considers the frequencies of the keywords as well.…”

“…After a critical analysis of these studies, we conclude the following:  All studies except [14] are applied on single documents.  Studies in [11], [12], and [14] are used the same dataset (EASC).…”

“… It can be seen from Table 1 that the study in [12] achieved better results on score-based approach compared to machine learning approaches. It is difficult to compare with the results of study [11] since different measures are used for evaluation.…”