Extractive Multi-Document Text Summarization by Using Binary Particle Swarm Optimization

被引：1
|
作者：
Potnurwar, Archana [1 ]
Pimpalshende, Anjusha [3 ]
Aote, Shailendra S. [2 ]
Bongirwar, Vrusbali [2 ]
机构：
[1] Priyadarshini Inst Engn & & Technol, Nagpur, Maharashtra, India
[2] Shri Ramdeobaba Coll Engn & Management, Nagpur, Maharashtra, India
[3] CMR Coll Engn & Technol, Hyderabad, India
来源：
BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS | 2020年 / 13卷 / 14期
关键词：
MULTI-DOCUMENT; TEXT SUMMARIZATION; SWARM INTELLIGENCE;
D O I：
10.21786/bbrc/13.14/8
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
The absence of a standard dataset and poor work for Hindi text summarization leads to develop a technique for better results. We have used a combination of Title feature, Sentence length, Sentence position, Numerical Data, Thematic word, Term frequency and Inverse Sentence Frequency for finding the results. Binary PSO is used for finding the optimal values of the features.
引用
收藏
页码：32 / 34
页数：3
相关论文
共 50 条
  • [1] Extractive multi-document text summarization using dolphin swarm optimization approach
    Srivastava, Atul Kumar
    Pandey, Dhiraj
    Agarwal, Alok
    MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (07) : 11273 - 11290
  • [2] Extractive multi-document text summarization using dolphin swarm optimization approach
    Atul Kumar Srivastava
    Dhiraj Pandey
    Alok Agarwal
    Multimedia Tools and Applications, 2021, 80 : 11273 - 11290
  • [3] 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
  • [4] Binary Particle Swarm Optimization with an improved genetic algorithm to solve multi-document text summarization problem of Hindi documents
    Aote, Shailendra S.
    Pimpalshende, Anjusha
    Potnurwar, Archana
    Lohi, Shantanu
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 117
  • [5] Parallelizing a multi-objective optimization approach for extractive multi-document text summarization
    Sanchez-Gomez, Jesus M.
    Vega-Rodriguez, Miguel A.
    Perez, Carlos J.
    JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2019, 134 : 166 - 179
  • [6] Multi-document extractive text summarization based on firefly algorithm
    Tomer, Minakshi
    Kumar, Manoj
    JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2022, 34 (08) : 6057 - 6065
  • [7] Survey on Extractive Text Summarization Methods with Multi-Document Datasets
    Varalakshmi, P. N. K.
    Kallimani, Jagadish S.
    2018 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2018, : 2113 - 2119
  • [8] Multi-document extractive text summarization: A comparative assessment on features
    Mutlu, Begum
    Sezer, Ebru A.
    Akcayol, M. Ali
    KNOWLEDGE-BASED SYSTEMS, 2019, 183
  • [9] Text Summarization as a Multi-objective Optimization Task: Applying Harmony Search to Extractive Multi-Document Summarization
    Bidoki, M.
    Fakhrahmad, M.
    Moosavi, M. R.
    COMPUTER JOURNAL, 2022, 65 (05): : 1053 - 1072
  • [10] Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach
    Sanchez-Gomez, Jesus M.
    Vega-Rodriguez, Miguel A.
    Perez, Carlos J.
    KNOWLEDGE-BASED SYSTEMS, 2018, 159 : 1 - 8
12345