Automatic Multi-Document Summarization for Indonesian Documents Using Hybrid Abstractive-Extractive Summarization Technique

被引:0
|
作者
Yapinus, Glorian [1 ]
Erwin, Alva [1 ]
Galinium, Maulahikmah [1 ]
Muliady, Wahyu [2 ]
机构
[1] Swiss German Univ, Fac Engn & Informat Technol, BSD, Tangerang, Indonesia
[2] Akon Teknol, BSD, Tangerang, Indonesia
关键词
Multi-Document Summarization; Abstractive Technique; Extractive Technique; Indonesian Documents;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper discusses the development of multi-document summarization for Indonesian documents by using hybrid abstractive-extractive summarization approach. Multi-document summarization is a technology that able to summarize multiple documents and present them in one summary. The method used in this research, hybrid abstractive-extractive summarization technique, is a summarization technique that is the combination of WordNet based text summarization (abstractive technique) and title word based text summarization (extractive technique). After an experiment with LSA as the comparison method, this research method successfully generated a well-compressed and readable summary with a fast processing time.
引用
收藏
页码:39 / 43
页数:5
相关论文
共 50 条
  • [21] Genetic Semantic Graph Approach for Multi-document Abstractive Summarization
    Khan, Atif
    Salim, Naomie
    Kumar, Yogan Jaya
    2015 FIFTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION PROCESSING AND COMMUNICATIONS (ICDIPC), 2015, : 173 - 181
  • [22] MeanSum : A Neural Model for Unsupervised Multi-Document Abstractive Summarization
    Chu, Eric
    Liu, Peter J.
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, 2019, 97
  • [23] A CLUSTERED SEMANTIC GRAPH APPROACH FOR MULTI-DOCUMENT ABSTRACTIVE SUMMARIZATION
    Khan, Atif
    Salim, Naomie
    Reafee, Waleed
    Sukprasert, Anupong
    Kumar, Yogan Jaya
    JURNAL TEKNOLOGI, 2015, 77 (18): : 61 - 72
  • [24] Multi-document Abstractive Summarization Based on Predicate Argument Structure
    Alshaina, S.
    John, Ansamma
    Nath, Aneesh G.
    2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, INFORMATICS, COMMUNICATION AND ENERGY SYSTEMS (SPICES), 2017,
  • [25] Automatic multi-document summarization for digital libraries
    Ou Shiyan
    Khoo, Christopher S. G.
    Goh, Dion H.
    PROCEEDINGS OF THE ASIA-PACIFIC CONFERENCE ON LIBRARY & INFORMATION EDUCATION & PRACTICE 2006: PREPARING INFORMATION PROFESSIONALS FOR LEADERSHIP IN THE NEW AGE, 2006, : 72 - +
  • [26] Unsupervised extractive multi-document text summarization using a Genetic Algorithm
    Neri-Mendoza, Veronica
    Ledeneva, Yulia
    Garcia-Hernandez, Rene Arnulfo
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (02) : 2397 - 2408
  • [27] Extractive multi-document summarization using relative redundancy and coherence scores
    Akhtar, Nadeem
    Beg, M. M. Sufyan
    Hussain, Md. Muzakkir
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 38 (05) : 6201 - 6210
  • [28] Topic modeling combined with classification technique for extractive multi-document text summarization
    Rajendra Kumar Roul
    Soft Computing, 2021, 25 : 1113 - 1127
  • [29] Topic modeling combined with classification technique for extractive multi-document text summarization
    Roul, Rajendra Kumar
    SOFT COMPUTING, 2021, 25 (02) : 1113 - 1127
  • [30] Extractive Multi-document Text Summarization Leveraging Hybrid Semantic Similarity Measures
    Bandaru, Rajesh
    Radhika, Dr. Y.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (09) : 844 - 852