Extractive Multi-Document Summarization: A Review of Progress in the Last Decade

被引:3
|
作者
Jalil, Zakia [1 ]
Nasir, Jamal Abdul [1 ]
Nasir, Muhammad [1 ]
机构
[1] Int Islamic Univ, Dept Comp Sci & Software Engn, Islamabad 44000, Pakistan
关键词
Semantics; Ontologies; Redundancy; Data mining; Task analysis; Natural language processing; Licenses; Abstractive summarization; clustering; extractive summarization; graph-based; machine learning; multi-document summarization; natural language processing; ontology; term-based; DIFFERENTIAL EVOLUTION; ARCHETYPAL ANALYSIS; MAXIMUM COVERAGE; TEXT; GRAPH; FRAMEWORK; REDUNDANCY; ALGORITHM; RELEVANCE; SEARCH;
D O I
10.1109/ACCESS.2021.3112496
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the tremendous growth in the number of electronic documents, it is becoming challenging to manage the volume of information. Much research has focused on automatically summarizing the information available in the documents. Multi-Document Summarization (MDS) is one approach that aims to extract the information from the available documents in such a concise way that none of the important points are missed from the summary while avoiding the redundancy of information at the same time. This study presents an extensive survey of extractive MDS over the last decade to show the progress of research in this field. We present different techniques of extractive MDS and compare their strengths and weaknesses. Research work is presented by category and evaluated to help the reader understand the work in this field and to guide them in defining their own research directions. Benchmark datasets and standard evaluation techniques are also presented. This study concludes that most of the extractive MDS techniques are successful in developing salient and information-rich summaries of the documents provided.
引用
下载
收藏
页码:130928 / 130946
页数:19
相关论文
共 50 条
  • [21] A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization
    Parnell, Jacob
    Unanue, Inigo Jauregi
    Piccardi, Massimo
    PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS), 2022, : 5112 - 5128
  • [22] MULTI-DOCUMENT VIDEO SUMMARIZATION
    Wang, Feng
    Merialdo, Bernard
    ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3, 2009, : 1326 - 1329
  • [23] On redundancy in multi-document summarization
    Calvo, Hiram
    Carrillo-Mendoza, Pabel
    Gelbukh, Alexander
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 34 (05) : 3245 - 3255
  • [24] Abstractive Multi-Document Summarization
    Ranjitha, N. S.
    Kallimani, Jagadish S.
    2017 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2017, : 1690 - 1693
  • [25] 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
  • [26] Extractive Multi-Document Text Summarization by Using Binary Particle Swarm Optimization
    Potnurwar, Archana
    Pimpalshende, Anjusha
    Aote, Shailendra S.
    Bongirwar, Vrusbali
    BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS, 2020, 13 (14): : 32 - 34
  • [27] Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
    Cho, Sangwoo
    Lebanoff, Logan
    Foroosh, Hassan
    Liu, Fei
    57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), 2019, : 1027 - 1038
  • [28] 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
  • [29] Multi-document Extractive Summarization Using Window-based Sentence Representation
    Zhang, Yong
    Er, Meng Joo
    Zhao, Rui
    2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 2015, : 404 - 410
  • [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