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 >

Ebrahim Heidary¹, Hamïd Parvïn^2,3,4,*, Samad Nejatian^5,6, Karamollah Bagherifard^1,6, Vahideh Rezaie^6,7

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2845-2861, 2021, DOI:10.32604/cmc.2021.014361

Abstract With the remarkable growth of textual data sources in recent years, easy, fast, and accurate text processing has become a challenge with significant payoffs. Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents, which must be done without losing important features and information. This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure. The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text, which improves the… More >

Sunqiang Hu¹, Xiaoyu Li¹, Yu Deng^1,*, Yu Peng¹, Bin Lin², Shan Yang³

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 145-158, 2021, DOI:10.32604/cmc.2021.017441

Abstract In recent years, many text summarization models based on pre-training methods have achieved very good results. However, in these text summarization models, semantic deviations are easy to occur between the original input representation and the representation that passed multi-layer encoder, which may result in inconsistencies between the generated summary and the source text content. The Bidirectional Encoder Representations from Transformers (BERT) improves the performance of many tasks in Natural Language Processing (NLP). Although BERT has a strong capability to encode context, it lacks the fine-grained semantic representation. To solve these two problems, we proposed a semantic supervision method based on… More >

Neeraj Kumar Sirohi, Mamta Bansal, S. N. Rajan

Journal on Big Data, Vol.3, No.2, pp. 97-97, 2021, DOI:10.32604/jbd.2021.041299

Abstract This article has no abstract. More >

Neeraj Kumar Sirohi^1,*, Mamta Bansal¹, S. N. Rajan²

Journal on Big Data, Vol.3, No.1, pp. 35-47, 2021, DOI:10.32604/jbd.2021.015954

Abstract Nowadays, data is very rapidly increasing in every domain such as social media, news, education, banking, etc. Most of the data and information is in the form of text. Most of the text contains little invaluable information and knowledge with lots of unwanted contents. To fetch this valuable information out of the huge text document, we need summarizer which is capable to extract data automatically and at the same time capable to summarize the document, particularly textual text in novel document, without losing its any vital information. The summarization could be in the form of extractive and abstractive summarization. The… More >

Ebrahim Heidary¹, Hamïd Parvïn^2,3,4,*, Samad Nejatian^5,6, Karamollah Bagherifard^1,6, Vahideh Rezaie^6,7, Zulkefli Mansor⁸, Kim-Hung Pho⁹

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1085-1101, 2021, DOI:10.32604/cmc.2021.013836

Abstract Taking into account the increasing volume of text documents, automatic summarization is one of the important tools for quick and optimal utilization of such sources. Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document. In this study, a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns. One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation… More >