Extracting easy to understand summary using differential evolution algorithm

Nandhini, K.; Balasundaram, S. R.

doi:10.1016/j.swevo.2013.12.004

Cited by 16 publications

(7 citation statements)

References 34 publications

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“…No GA length, coverage, informativeness and redundancy Huang et al [28] Yes No -Coverage, significance, redundancy and coherence Aliguliyev [29] No No PSO Clustering Nandhini and Balasundaram [30] No No DE Informative and cohesion Lozano et al [31] No No CHC Greedy Coverage and redundancy reduction Alguliyev et al [13] No No DE Relevance, redundancy and length Mirshojaei and Masoomi [32] No No CSOA -Rautray et al [33] No No DE, PSO Redundant reducing and maximising relevancy Goleman et al [34] No sentences. Here, N is the number of documents and n is the number of sentences, while s i is the ith sentence in S. Set T = {t 1, t 2,,,, t m } represents all terms in S, S i = [wi 1 , wi 2 ,..., wi m ].…”

Section: Work Optimisation Informationmentioning

confidence: 99%

“…Aliguliyev [29] used the PSO algorithm for text clustering to produce a rich summary and proposed a generic, multi-document summarisation method by optimising the similarities of the clusters. Nandhini and Balasundaram [30] used the DE algorithm to create an easy-to-understand textual summary. Lozano et al [31] proposed an algorithm to provide an extractive summation from multiple documents that optimise the coverage and redundancy variables through binary optimisation.…”

Section: Related Workmentioning

confidence: 99%

“…The coverage value of the summary reflects the extent to which it covers the content in the original documents. Umam et al [30] stated that the coverage calculation should consider content similarities between the candidate summary document and the main document. By calculating the similarity between each sentence in summary and the main content of the original document, the importance of each summary sentence in the original document can be assessed.…”

Section: Problem Definitionmentioning

confidence: 99%

See 2 more Smart Citations

A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

Hark

Uçkan

Karcı

2022

Journal of Information Science

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Automatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.

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Section: Work Optimisation Informationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

See 1 more Smart Citation

A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

Hark

Uçkan

Karcı

2022

Journal of Information Science

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“…Hence, differential evolution algorithm is used to look for the optimization combination of the parameters K and α of variational mode decomposition. Differential evolution algorithm includes three operations: mutation operation, crossover operation, and selection operation [11,12]. In mutation operation, the mutation vector is generated by the mutation operator.…”

Section: Adaptive Variational Mode Decomposition Technique With Differential Evolution Algorithmmentioning

confidence: 99%

An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis

Wang

2021

Shock and Vibration

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Variational mode decomposition is an adaptive nonrecursive signal decomposition and time-frequency distribution estimation method. The improper selection of the decomposition number will cause under decomposition or over decomposition, and the improper selection of the penalty factor will affect the bandwidth of modal components, so it is very necessary to look for the optimal parameter combination of the decomposition number and the penalty factor of variational mode decomposition. Hence, differential evolution algorithm is used to look for the optimization combination of the decomposition number and the penalty factor of variational mode decomposition because differential evolution algorithm has a good ability at global searching. The method is called adaptive variational mode decomposition technique with differential evolution algorithm. Application analysis and discussion of the adaptive variational mode decomposition technique with differential evolution algorithm are employed by combining with the experiment. The conclusions of the experiment are that the decomposition performance of the adaptive variational mode decomposition technique with differential evolution algorithm is better than that of variational mode decomposition.

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“…Only a handful of text summarization methods so far have integrated readability assessment to select not only the most informative, but also the most comprehensible sentences. Nandhini and Balasundaram (2014) designed one of such approaches. They represent each document as a set of 4 informative features (sentence position, title similarity, etc.)…”

Section: Related Workmentioning

confidence: 99%

Towards Adaptive Text Summarization: How Does Compression Rate Affect Summary Readability of L2 Texts?

Vodolazova

Lloret

2019

Proceedings - Natural Language Processing in a Deep Learning World

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This paper addresses the problem of readability of automatically generated summaries in the context of second language learning. For this we experimented with a new corpus of level-annotated simplified English texts. The texts were summarized using a total of 7 extractive and abstractive summarization systems with compression rates of 20%, 40%, 60% and 80%. We analyzed the generated summaries in terms of lexical, syntactic and length-based features of readability, and concluded that summary complexity depends on the compression rate, summarization technique and the nature of the summarized corpus. Our experiments demonstrate the importance of choosing appropriate summarization techniques that align with users needs and language proficiency.

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Extracting easy to understand summary using differential evolution algorithm

Cited by 16 publications

References 34 publications

A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis

Towards Adaptive Text Summarization: How Does Compression Rate Affect Summary Readability of L2 Texts?

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