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  • Open Access


    Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

    Ebrahim Heidary1, Hamïd Parvïn2,3,4,*, Samad Nejatian5,6, Karamollah Bagherifard1,6, Vahideh Rezaie6,7

    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 >

  • Open Access


    A Semantic Supervision Method for Abstractive Summarization

    Sunqiang Hu1, Xiaoyu Li1, Yu Deng1,*, 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 >

  • Open Access


    Retraction Notice to: Recent Approaches for Text Summarization Using Machine Learning & LSTM0

    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 >

  • Open Access


    RETRACTED: Recent Approaches for Text Summarization Using Machine Learning & LSTM0

    Neeraj Kumar Sirohi1,*, 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 >

  • Open Access


    Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns

    Ebrahim Heidary1, Hamïd Parvïn2,3,4,*, Samad Nejatian5,6, Karamollah Bagherifard1,6, Vahideh Rezaie6,7, 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 >

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