ArA*summarizer: An Arabic text summarization system based on subtopic segmentation and using an A* algorithm for reduction

Abstract

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 condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster‐based, and graph‐based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall‐oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n‐gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works.

Leveraging DistilBERT for Summarizing Arabic Text: An Extractive Dual-Stage Approach

“…The latter method is popular (i.e., especially in news domain [23]) and seems a more straightforward method, which tends to produce a higher efficiency summary than the abstractive-based summary [24]. Diving into the extractive method that we consider in this paper, there are various techniques used in addressing Arabic summarization, such as statistical/linguistic-based [8], [15], [9], [6], semantic/query-based [12], [7], [8], [21], [9], graph/optimization-based [10], [11], [25], [12], and machine learning [5], [13], [14], [20], [19], [15], [26], [27], [28]. For a detailed review of these techniques for the Arabic ATS, we recommend that researchers refer to these recent works [8], [5], [1].…”

“…Various researchers in the machine learning communities have studied ATS problem with different learning techniques, including e.g., classical supervised learning [16], [19], unsupervised clustering-based learning [13], [15], [5], [10], reinforcement-based learning [20], and deep learning based on artificial neural networks [14], [32], [33], [34], [31], [29], [35], [36], [18], [30], [26], [27], [28]. For instance, [16] developed an Arabic text summarizer based on the adaptive boosting model (a.k.a, AdaBoost, which is supervised-based learning used typically to optimize the weak decision-tree classifiers).…”

“…To our knowledge, there exists a limited handful of Arabic text summarization approaches [45], [15], [46], [9], [10], [13] that are comparable to our ArDBertSum. In Table 7, we listed these extractive-based approaches as baselines and classified them into either heuristic or non-heuristic techniques.…”

“…might render these summarizers prone to inaccuracies and bias in evaluation. Heuristics in this context would include (1) training the summarizers using the EASC dataset, or taking advantage of the overall keyword-tokens across multiple original (EASC) documents [13], or examining this specific EASC dataset to extract useful heuristic (e.g., heuristic for the A* algorithm as in [10]). Towards the generality, our ArDBertSum is entirely nonheuristic and depends on a fine-tuned version of a pre-trained language understanding model (i.e., DistilBERT).…”

“…The abstractive technique, however, can generate new texts that may not exist in the original documents. Broadly, there are different extractive-based techniques through which Arabic ATS is achieved by many researchers, e.g., [6], [7], [8], [9], [10], [11], [12], [5], [13], [14], [15]. These techniques can be categorized into different approaches: the statistical approach, the semantic approach, the machine learning approach, and the meta-heuristic approach.…”