Applying Deep Learning for Arabic Keyphrase Extraction

Helmy, Muhammad; Vigneshram, R.M.; Serra, Giuseppe; Tasso, Carlo

doi:10.1016/j.procs.2018.10.486

Cited by 14 publications

(9 citation statements)

References 33 publications

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“…For comparison with Arabic research that uses deep learning to summarize texts, we relied on the latest Arabic research based on deep learning that approximates the task of summarizing texts, which is extracting keyphrases from text [27]. The length of the extracted key phrases, two to three words, will correspond to the length of the titles generated by the model we worked on.…”

Section: Arabic Research Results Comparisonmentioning

confidence: 99%

“…Since the idea of the research in [27] is close to our research and uses deep learning, it was appropriate to compare the two researches. We applied our model (pointer-generator with a length penalty) to the test dataset of [27], which has 940 entries. The results of the summary contained many (UNK) tokens, which means the word is outside the dictionary of the model, thus the comparison was not possible.…”

Section: Arabic Research Results Comparisonmentioning

confidence: 99%

“…1. The dataset was pre-processed in [27] in a way that effected the original form of the words, such as: a. Writing letters ‫،أ"‬ ‫،إ‬ ‫،آ‬ ‫"ا‬ in the normal form ‫.…”

Section: Arabic Research Results Comparisonmentioning

confidence: 99%

“…While contributions to the Arabic language using sequence-to-sequence deep learning framework are still limited, Elmadani et al [25] showcased how the fine-tuned pretrained BERT model [26] can be applied to the Arabic language to both construct the first documented model for abstractive Arabic text summarization, and showed its performance in Arabic extractive summarization. In similar research domains, Helmy et al [27] proposed a deep learning based approach for Arabic keyphrase extraction. It achieves better performance compared to the related competitive approaches.…”

Section: Related Workmentioning

confidence: 99%

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Arabic text summarization using deep learning approach

Al-Maleh

Desouki

2020

J Big Data

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Natural language processing has witnessed remarkable progress with the advent of deep learning techniques. Text summarization, along other tasks like text translation and sentiment analysis, used deep neural network models to enhance results. The new methods of text summarization are subject to a sequence-to-sequence framework of encoder–decoder model, which is composed of neural networks trained jointly on both input and output. Deep neural networks take advantage of big datasets to improve their results. These networks are supported by the attention mechanism, which can deal with long texts more efficiently by identifying focus points in the text. They are also supported by the copy mechanism that allows the model to copy words from the source to the summary directly. In this research, we are re-implementing the basic summarization model that applies the sequence-to-sequence framework on the Arabic language, which has not witnessed the employment of this model in the text summarization before. Initially, we build an Arabic data set of summarized article headlines. This data set consists of approximately 300 thousand entries, each consisting of an article introduction and the headline corresponding to this introduction. We then apply baseline summarization models to the previous data set and compare the results using the ROUGE scale.

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Section: Arabic Research Results Comparisonmentioning

confidence: 99%

Section: Arabic Research Results Comparisonmentioning

confidence: 99%

Section: Arabic Research Results Comparisonmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Arabic text summarization using deep learning approach

Al-Maleh

Desouki

2020

J Big Data

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“…A dropout technique was implemented between Bi-LSTM and the dense layer to prevent overfitting. The evaluation results showed that the proposed approach achieves state-of-the-art performance in the Arabic KPE domain [15]. Also, Samir Boukil et al, proposed a method for Arabic text classification.…”

Section: Sagheer Et Al Discussed Applying Deep Learning Techniquesmentioning

confidence: 95%

A Proposed Deep Learning based Framework for Arabic Text Classification

Sayed¹,

Abdelkader²,

Khedr³

et al. 2022

IJACSA

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Deep learning has become one of the crucial trends in the modern era due to the huge amount of data that has become available. This paper aims to investigate and improve a generic framework for Arabic Text Classification (ATC) with different deep learning techniques. Besides, it deals directly with a word in its original style as a basic unit of modern Arabic sentence and on a different level of N-grams versus a combination of Intersected Consecutive Word proposed method (ICW). However, it aimed to discuss the results of the different experiments for the enhancements of the proposed method on different deep learning algorithms such as Scaled Conjugate Gradient (SCG) and Gradient descent with momentum and adaptive learning rate backpropagation (GDX) on ATC. The results showed that the proposed framework applied with the SCG algorithm and TF-IDF outperforms the GDX algorithm with an accuracy ratio of 90.65%.

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