Opposition Differential Evolution Based Method for Text Summarization

Abuobieda, Albaraa; Salim, Naomie; Kumar, Yogan Jaya; Osman, Ahmed Hamza

doi:10.1007/978-3-642-36546-1_50

Cited by 12 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This summarizer uses term frequencies to calculate the relative importance of each sentence in a document. It is widely used in related studies [43,44,45,46,47] as a benchmark method for automatic summarisation systems.…”

Section: Comparison With Benchmark Methods and Related Workmentioning

confidence: 99%

“…The majority of today's implemented extractive summarizers adopt sentence scoring or graph-based sentence ranking. Prevailing sentence scoring and selection techniques in text summarization include graph-based representation [52], text semantic analysis [3], semantic similarity [5], sentence clustering [53], fuzzy reasoning [44,49], sentence regression [55] and differential evolution [46,54]. Several related works conducted a comparative study on a range of sentence scoring methods by examining the performance of their combinations for text summarization [51,52].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

Mohamed

Oussalah

2019

Information Processing & Management

View full text Add to dashboard Cite

Automatic text summarization attempts to provide an effective solution to today's unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL and ESA methods are used to achieve sentence semantic parsing and to represent its words as a vector of weighted Wikipedia concepts, respectively. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. Results also tell that the system is scalable, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectoral semantic representation when combined with the crowd-sourced knowledge base in Wikipedia.

show abstract

Section: Comparison With Benchmark Methods and Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

Mohamed

Oussalah

2019

Information Processing & Management

View full text Add to dashboard Cite

show abstract

“…Finally, the top-weighted sentence in every cluster is picked out to form the summary until a user-preferred summary length is met. An evolutionary algorithm called Differential Evolution algorithm was also used to optimize data clustering process and could increase the quality of the generated text summaries (Abuobieda et al, 2013b).…”

Section: Cluster Based Methodsmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…It is worth mentioning that all evaluation scores extracted using ROUGE ate statistically significant at the 95% confidence interval [17]. For result analysis, all measures were used to evaluate the methods performance, with special attention to f-measure due it balances both recall and precision scores.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

Automatic Summarization of News Articles in Mobile Devices

Cabral

Lima

Lins

et al. 2015

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)

View full text Add to dashboard Cite

Smartphones and tablets provide access to the Web anywhere and anytime. Automatic Text Summarization techniques aim to extract the fundamental information in documents. Making automatic summarization work in portable devices is a challenge, in several aspects. This paper presents an automatic summarization application for Android devices. The proposed solution is a multi-feature language independent summarization application targeted at news articles. Several evaluation assessments were conducted and indicate that the proposed solution provides good results.

show abstract

Opposition Differential Evolution Based Method for Text Summarization

Cited by 12 publications

References 10 publications

SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

A Review on Automatic Text Summarization Approaches

Automatic Summarization of News Articles in Mobile Devices

Contact Info

Product

Resources

About