T. Ganier, J. Hu, A. Pizzi^*

Journal of Renewable Materials, Vol.1, No.1, pp. 79-82, 2013, DOI:10.7569/JRM.2012.634101

Abstract Linear vibration welding of extractive rich Paduk wood from central Africa containing a high proportion of a native mixture of water-insoluble extractives, or of low water solubility, has been shown to yield joints of much upgraded water resistance. This has been shown to be due to the protecting infl uence the extractives from the wood itself has on the welded interphase, due to their inherent water repellence. Joints of unusually high percentage wood failure but modest strength were obtained; Paduk wood brittleness apparently yielding weld line strengths always higher than that of the surrounding wood itself. This indicated that Paduk… More >

P. Sharmila^1,*, C. Deisy¹, S. Parthasarathy²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 377-393, 2023, DOI:10.32604/csse.2023.027481

Abstract With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading. Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline. Abstractive summarization task is framed as seq2seq modeling. Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper. The novelty… 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 >

Wissem Sahmim, Georges Eid, Febrina Dellarose Boer, Hubert Chapuis, Philippe Gérardin, Christine Gérardin-Charbonnier^*

Journal of Renewable Materials, Vol.10, No.6, pp. 1491-1503, 2022, DOI:10.32604/jrm.2022.016364

Abstract This study evaluated the methods of grafting commercial catechin with fatty acids, namely capric acid (C10), lauric acid (C12), and myristic acid (C14) through esterification. Specimens of beech wood (Fagus sylvatica L.) were impregnated with catechin and modified catechin-fatty acids, separately, at a 5% concentration diluted in ethanol using vacuum pressure treatment and subjected to leaching. The weight percentage gain before leaching (WPG), after leaching (WPGAL), and weight loss due to leaching (PL) were investigated. Both leached and unleached samples were tested against white-rot fungi (Trametes versicolor) in Petri-dishes for twelve weeks. Results show that samples treated with modified catechin-fatty… More > Graphic Abstract

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 >

Sarah S. Alrumiah^*, Amal A. Al-Shargabi

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6205-6221, 2022, DOI:10.32604/cmc.2022.021780

Abstract Nowadays, people use online resources such as educational videos and courses. However, such videos and courses are mostly long and thus, summarizing them will be valuable. The video contents (visual, audio, and subtitles) could be analyzed to generate textual summaries, i.e., notes. Videos’ subtitles contain significant information. Therefore, summarizing subtitles is effective to concentrate on the necessary details. Most of the existing studies used Term Frequency–Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) models to create lectures’ summaries. This study takes another approach and applies Latent Dirichlet Allocation (LDA), which proved its effectiveness in document summarization. Specifically, the proposed… 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 >

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 >

Resa Martha^1,2, Mahdi Mubarok^1,2, Wayan Darmawan², Wasrin Syafii², Stéphane Dumarcay¹, Christine Gérardin Charbonnier¹, Philippe Gérardin^1,*

Journal of Renewable Materials, Vol.9, No.3, pp. 399-449, 2021, DOI:10.32604/jrm.2021.014286

Abstract As a tropical archipelagic country, Indonesia’s forests possess high biodiversity, including its wide variety of wood species. Valorisation of biomolecules released from woody plant extracts has been gaining attractive interests since in the middle of 20th century. This paper focuses on a literature review of the potential valorisation of biomolecules released from twenty wood species exploited in Indonesia. It has revealed that depending on the natural origin of the wood species studied and harmonized with the ethnobotanical and ethnomedicinal knowledge, the extractives derived from the woody plants have given valuable heritages in the fields of medicines and pharmacology. The families… 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 >