Multi-document Summarization Using a Clustering-Based Hybrid Strategy

Nie, Yu; Ji, Donghong; Yang, Lingpeng; Niu, Zheng-Yu; He, Tingting

doi:10.1007/11880592_53

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Some researchers employ clustering-based hybrid strategy to combine local and global search for sentence selection (Nie et al, 2006). This approach does not depend only on similarity to cluster for sentence selection but also considers the overall document content similarity.…”

Section: Cluster Based Methodsmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

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Section: Cluster Based Methodsmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Some researchers employ clustering-based hybrid strategy (Yu et al, 2006) to combine local and global search for sentence selection. This approach does not depend only on similarity to cluster for sentence selection but also considers the overall document content similarity.…”

Section: Cluster Based Methodmentioning

confidence: 99%

Automatic Multi Document Summarization Approaches

Kumar

Salim²

2012

Journal of Computer Science

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Problem statement: Text summarization can be of different nature ranging from indicative summary that identifies the topics of the document to informative summary which is meant to represent the concise description of the original document, providing an idea of what the whole content of document is all about. Approach: Single document summary seems to capture both the information well but it has not been the case for multi document summary where the overall comprehensive quality in presenting informative summary often lacks. It is found that most of the existing methods tend to focus on sentence scoring and less consideration is given to the contextual information content in multiple documents. Results: In this study, some survey on multi document summarization approaches has been presented. We will direct our focus notably on four well known approaches to multi document summarization namely the feature based method, cluster based method, graph based method and knowledge based method. The general ideas behind these methods have been described. Conclusion: Besides the general idea and concept, we discuss the benefits and limitations concerning these methods. With the aim of enhancing multi document summarization, specifically news documents, a novel type of approach is outlined to be developed in the future, taking into account the generic components of a news story in order to generate a better summary.

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“…Majority authors [1] - [6], [9] - [12], [14], [16] - [21], [24], [25], [32], [35], [41], [48], [51], [55], [64], [65], [66], [67], [71], [80], have used DUC to observe the performance of their technique. TAC-Text Analysis Conference: Like DUC, TAC is also a collection of benchmarked documents from multiple sources, accompanied by human expert-generated summaries for reference.…”

Section: E Secondary Studies Conducted In Mdsmentioning

confidence: 99%

“…The metrics compare an automatically produced summary or translation against a reference or a set of references (humanproduced) summary or translation. Authors in [2] - [6], [8] - [12], [14] - [21], [24], [25], [29] [35], [39], [41], [43], [48], [51], [54], [55], [62]- [68] , [70], [71], [80] used ROUGE for the validation of their results. BLEU-Bilingual Evaluation Understudy: It is a special algorithm for quality evaluation of machine-translated text between natural languages.…”

Section: Evaluation Techniquesmentioning

confidence: 99%

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

Jalil

Nasir

2021

IEEE Access

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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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Multi-document Summarization Using a Clustering-Based Hybrid Strategy

Cited by 4 publications

References 6 publications

A Review on Automatic Text Summarization Approaches

A Review on Automatic Text Summarization Approaches

Automatic Multi Document Summarization Approaches

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

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