Hala J. Alshahrani¹, Khaled Tarmissi², Ayman Yafoz³, Abdullah Mohamed⁴, Manar Ahmed Hamza^5,*, Ishfaq Yaseen⁵, Abu Sarwar Zamani⁵, Mohammad Mahzari⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3203-3219, 2023, DOI:10.32604/iasc.2023.034848

Abstract The term ‘executed linguistics’ corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems. The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights. The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users. The Automatic Text Summarization (ATS) process reduces the primary size of the text without losing any basic components of the data. The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning (ALTS-MLODL) model. The… More >

Y. C. A. Padmanabha Reddy¹, Shyam Sunder Reddy Kasireddy², Nageswara Rao Sirisala³, Ramu Kuchipudi⁴, Purnachand Kollapudi^5,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6389-6409, 2023, DOI:10.32604/cmc.2023.034072

Abstract The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages. The digital document needs to be evaluated physically through the Cross-Language Text Summarization (CLTS) involved in the disparate and generation of the source documents. Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents. The digital documents need to be processed with the contextual semantic data with the decoding scheme. This paper presented a multilingual cross-language processing of the documents with the abstractive and summarising of the documents. The proposed model is represented as the Hidden Markov… More >

Supriya Gupta^*, Aakanksha Sharaff, Naresh Kumar Nagwani

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2333-2349, 2023, DOI:10.32604/csse.2023.030385

Abstract Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort. Evaluating and selecting the most informative sentences from biomedical articles is always challenging. This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information. The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model. The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them. The proposed framework utilizes the top… More >

Nada Ali Hakami¹, Hanan Ahmed Hosni Mahmoud^2,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3697-3710, 2023, DOI:10.32604/cmc.2023.031525

Abstract A worthy text summarization should represent the fundamental content of the document. Recent studies on computerized text summarization tried to present solutions to this challenging problem. Attention models are employed extensively in text summarization process. Classical attention techniques are utilized to acquire the context data in the decoding phase. Nevertheless, without real and efficient feature extraction, the produced summary may diverge from the core topic. In this article, we present an encoder-decoder attention system employing dual attention mechanism. In the dual attention mechanism, the attention algorithm gathers main data from the encoder side. In the dual attention model, the system… More >

Mohamed Ali Rakrouki^1,*, Nawaf Alharbe¹, Mashael Khayyat², Abeer Aljohani¹

Computer Systems Science and Engineering, Vol.45, No.1, pp. 395-408, 2023, DOI:10.32604/csse.2023.029032

Abstract Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights.… More >

Hanan A. Hosni Mahmoud^1,*, Alaaeldin M. Hafez²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5121-5136, 2022, DOI:10.32604/cmc.2022.031485

Abstract A substantial amount of textual data is present electronically in several languages. These texts directed the gear to information redundancy. It is essential to remove this redundancy and decrease the reading time of these data. Therefore, we need a computerized text summarization technique to extract relevant information from group of text documents with correlated subjects. This paper proposes a language-independent extractive summarization technique. The proposed technique presents a clustering-based optimization technique. The clustering technique determines the main subjects of the text, while the proposed optimization technique minimizes redundancy, and maximizes significance. Experiments are devised and evaluated using BillSum dataset for… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4231-4244, 2022, DOI:10.32604/cmc.2022.030090

Abstract In recent research, deep learning algorithms have presented effective representation learning models for natural languages. The deep learning-based models create better data representation than classical models. They are capable of automated extraction of distributed representation of texts. In this research, we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module, and also addresses memory issues that were not addresses before. The proposed model employs a tree structured mechanism to generate the phrase and text embedding. The proposed architecture mimics the tree configuration of the text-texts and provide better… More >

P. Perumal^*

Computer Systems Science and Engineering, Vol.43, No.1, pp. 203-218, 2022, DOI:10.32604/csse.2022.020459

Abstract With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased. Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task. In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR). Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering with the generation of association… More >

Ahmed S. Almasoud¹, Siwar Ben Haj Hassine², Fahd N. Al-Wesabi^2,3, Mohamed K. Nour⁴, Anwer Mustafa Hilal⁵, Mesfer Al Duhayyim⁶, Manar Ahmed Hamza^5,*, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5799-5815, 2022, DOI:10.32604/cmc.2022.024556

Abstract Due to the advanced developments of the Internet and information technologies, a massive quantity of electronic data in the biomedical sector has been exponentially increased. To handle the huge amount of biomedical data, automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative data. So, multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated information. This paper presents a Deep Learning based Attention Long… More >

Surbhi Bhatia^*, Mohammed AlOjail

Computer Systems Science and Engineering, Vol.42, No.2, pp. 779-794, 2022, DOI:10.32604/csse.2022.022579

Abstract Opinion summarization recapitulates the opinions about a common topic automatically. The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text. The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining. This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory (RNN-LSTM) deep learning model for short and exact synopsis using seq2seq model. It presents a… More >