Automatic text summarization based on multi-agent particle swarm optimization

Asgari, Hamed; Masoumi, Behrooz; Sheijani, Omid Sojoodi

doi:10.1109/iraniancis.2014.6802592

Cited by 23 publications

(6 citation statements)

References 12 publications

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“…They had taken into account three factors: topic relation, cohesion and readability, and used the firefly optimization algorithm to address these problems. In this sequence, many other researchers (Asgari et al, 2014;Rautray & Balabantaray, 2017;Rautray & Balabantaray, 2018;Shareghi & Hassanabadi, 2008;Verma & Om, 2019c) have also addressed these problems using different optimization approaches. A simple particle swarm optimization-based summarization model was introduced by Binwahlan et al (Binwahlan et al, 2009).…”

Section: Related Workmentioning

confidence: 99%

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

Verma

Pal

2022

Expert Systems

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Document summarization is an important task in natural language processing that helps deal with the problem of information overload occurring due to the existence of redundant content. Summary generation with highly relevant contents and maximum coverage is particularly challenging which can only be achieved when redundancy is minimized. This article introduces a novel approach for automatic text summarization based on sentence scoring and collaborative ranking to produce summaries with minimal redundancy and improved overall performance of summarization. The proposed model is a fusion of weighted and unweighted features‐based sentence scoring methods. To learn optimal weights of text features, it has been modelled as an optimization problem. Moreover, the proposed model exploits the strength of collaborative ranking to generate the summary of a given document. Three similarity factors (proximity, significance and singularity)‐based models have been employed to find the similarity between weighted and unweighted sentence scores. The results of the comparison experiment demonstrate that the proposed (PS + Jac) method generates a closer summary to the reference summary with minimal redundant contents. On average, the proposed (PS + Jac) method generates the summaries with 61% accurate contents with greater improved rates up to 40%. The statistical testing also confirms that the performance improvement is significant at a 5% level of significance.

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Section: Related Workmentioning

confidence: 99%

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

Verma

Pal

2022

Expert Systems

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“…Inspired by this, we intend to investigate the optimal combinations of sentence scoring methods by modelling as an optimization problem and attempt to globally solve the problem. In recent years, many metaheuristic approaches like Particle Swarm Optimization (PSO) [5][6][7][8][9][10][11], Genetic Algorithm (GA) [12][13][14][15], Harmony Search Algorithm (HSA) [16], Cat Swarm Optimization (CSO) [17], Cuckoo Search (CS) [18], Multicriteria Optimization (MCO) [19] and Jaya [20] have been used to find the optimal weights for scoring methods or relevant sentences for summary generation. These metaheuristic approaches require significant computational effort for tuning a large number of controlling parameters.…”

Section: Motivationmentioning

confidence: 99%

“…Algiliev et al [6] 2011 PSO Multi He et al [26] 2011 Manifold ranking Multi Alguliev et al [27] 2012 DE Multi Mendoza et al [28] 2013 MA Single Asgari et al [7] 2014 PSO Single Khan et al [13] 2015 GA Multi Meena and Gopalani [15] 2015…”

Section: Ga Multimentioning

confidence: 99%

A novel approach for text summarization using optimal combination of sentence scoring methods

Verma

2019

Sādhanā

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In this paper, a novel multi-document summarization scheme based on metaheuristic optimization is introduced that generates a summary by extracting salient and relevant sentences from a collection of documents. The proposed work generates optimal combinations of sentence scoring methods and their respective optimal weights to extract the sentences with the help of a metaheuristic approach known as teaching-learning-based optimization. In addition, the proposed scheme is compared to two summarization methods that use different metaheuristic approaches. The experimental results show the efficacy of the proposed summarization scheme.

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“…The majority of SI-based summarization studies used particle swarm optimization (PSO). In these studies, PSO algorithms were used to select summary sentences [53,58] or set the weight of each feature extracted from the text to be summarized [54]. Alguliev et al [53] proposed an optimization model to solve the summarization problem.…”

Section: Swarm-intelligence-based Approachesmentioning

confidence: 99%

“…Binwahlan et al [54] used a PSO algorithm as a machine learning technique and ROUGE-1 as a fitness function to investigate the best features' weights. Asgari et al [58] proposed an extractive single-document summarization method based on a multi-agent PSO.…”

Section: Swarm-intelligence-based Approachesmentioning

confidence: 99%

Solving Multi-Document Summarization as an Orienteering Problem

Al-Saleh

Menaï

2018

Algorithms

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With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information overload. This article proposes a novel extractive graph-based approach to solve the multi-document summarization (MDS) problem. To optimize the coverage of information in the output summary, the problem is formulated as an orienteering problem and heuristically solved by an ant colony system algorithm. The performance of the implemented system (MDS-OP) was evaluated on DUC 2004 (Task 2) and MultiLing 2015 (MMS task) benchmark corpora using several ROUGE metrics, as well as other methods. Its comparison with the performances of 26 systems shows that MDS-OP achieved the best F-measure scores on both tasks in terms of ROUGE-1 and ROUGE-L (DUC 2004), ROUGE-SU4, and three other evaluation methods (MultiLing 2015). Overall, MDS-OP ranked among the best 3 systems.

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Automatic text summarization based on multi-agent particle swarm optimization

Cited by 23 publications

References 12 publications

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

A novel approach for text summarization using optimal combination of sentence scoring methods

Solving Multi-Document Summarization as an Orienteering Problem

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