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.
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页码:130928 / 130946
页数:19
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