Deep Learning Based Extractive Text Summarization: Approaches, Datasets and Evaluation Measures

被引:0
|
作者
Suleiman, Dima [1 ,2 ]
Awajan, Arafat A. [1 ]
机构
[1] Univ Jordan, Dept Informat Technol, Amman, Jordan
[2] Princess Sumaya Univ Technol, King Hussein Fac Comp Sci, Comp Sci Dept, Amman, Jordan
关键词
Deep Learning; Extractive text summarization; Recurrent Neural Network; Convolutional Neural Network; Attention Mechanizm; Restricted Boltzmann Machine; Variation Auto-Encoder; LSTM; GRU; ROUGE;
D O I
10.1109/snams.2019.8931813
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recently, the number of online documents witness huge increase in volume. Thus, these documents need to be summarized in order to be effective. This paper reviews the most recent extractive text summarization approaches that are based on deep learning techniques. These approaches are classified into three categories based on deep learning techniques which are Restricted Boltzmann Machine, Variation Auto-Encoder and Recurrent Neural Network. The mostly used datasets for extractive summarizations are Daily Mail and DUC2002. Moreover, ROUGE is the mainly used evaluation measure to assess the quality of the extractive summarization process. The results show that SummaRuNNer approach which is based on Gated Recurrent Unit Recurrent Neural Network achieved the highest values for ROUGE1, ROUGE2 and ROUGE-L over Daily Mail datasets. On the other hand, the approach that is based on Recurrent Neural Network achieved the best results over DUC2002 datasets in term of ROUGE1 and ROUGE2.
引用
收藏
页码:204 / 210
页数:7
相关论文
共 50 条
  • [1] Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges
    Suleiman, Dima
    Awajan, Arafat
    [J]. Mathematical Problems in Engineering, 2020, 2020
  • [2] Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges
    Suleiman, Dima
    Awajan, Arafat
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020
  • [3] Extractive Text Summarization using Deep Learning
    Shirwandkar, Nikhil S.
    Kulkarni, Samidha
    [J]. 2018 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION (ICCUBEA), 2018,
  • [4] A Survey of Text Summarization Approaches Based on Deep Learning
    Sheng-Luan Hou
    Xi-Kun Huang
    Chao-Qun Fei
    Shu-Han Zhang
    Yang-Yang Li
    Qi-Lin Sun
    Chuan-Qing Wang
    [J]. Journal of Computer Science and Technology, 2021, 36 : 633 - 663
  • [5] A Survey of Text Summarization Approaches Based on Deep Learning
    Hou, Sheng-Luan
    Huang, Xi-Kun
    Fei, Chao-Qun
    Zhang, Shu-Han
    Li, Yang-Yang
    Sun, Qi-Lin
    Wang, Chuan-Qing
    [J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2021, 36 (03) : 633 - 663
  • [6] Extractive text summarization using deep learning approach
    Yadav A.K.
    Singh A.
    Dhiman M.
    Vineet
    Kaundal R.
    Verma A.
    Yadav D.
    [J]. International Journal of Information Technology, 2022, 14 (5) : 2407 - 2415
  • [7] Deep Extractive Text Summarization
    Bhargava, Rupal
    Sharma, Yashvardhan
    [J]. INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE, 2020, 167 : 138 - 146
  • [8] An Intelligent Tree Extractive Text Summarization Deep Learning
    AlArfaj, Abeer Abdulaziz
    Mahmoud, Hanan Ahmed Hosni
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 73 (02): : 4231 - 4244
  • [9] A Framework for Extractive Text Summarization Based on Deep Learning Modified Neural Network Classifier
    Muthu, Balaanand
    Sivaparthipan, C. B.
    Kumar, Priyan Malarvizhi
    Kadry, Seifedine Nimer
    Hsu, Ching-Hsien
    Sanjuan, Oscar
    Gonzalez Crespo, Ruben
    [J]. ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2021, 20 (03)
  • [10] A Survey of Extractive Arabic Text Summarization Approaches
    Lagrini, Samira
    Redjimi, Mohammed
    Aziz, Nabiha
    [J]. ARABIC LANGUAGE PROCESSING: FROM THEORY TO PRACTICE, 2018, 782 : 159 - 171