Comparative Study Between Two Swarm Intelligence Automatic Text Summaries

Boudia, Mohamed Amine; Hamou, Reda Mohamed; Amine, Abdelmalek

doi:10.4018/ijamc.2018010102

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…The final ranking of sentences is calculated using collaborative ranking. In a comparative study between two bio-inspired approaches based on swarm intelligence for automatic text summaries: Social Spiders and Social Bees [22], two techniques, scoring of phrases and similarity, are used for eliminating redundant phrases.…”

Section: Related Workmentioning

confidence: 99%

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Monte

Lanzarini

Bariviera

et al. 2019

Entropy

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Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.

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Section: Related Workmentioning

confidence: 99%

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Monte

Lanzarini

Bariviera

et al. 2019

Entropy

View full text Add to dashboard Cite

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A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection

Kyaw

Limsiroratana

Sattayaraksa

2022

International Journal of Applied Metaheuristic Computing

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Algorithmic – based search approach is ineffective at addressing the problem of multi-dimensional feature selection for document categorization. This study proposes the use of meta heuristic based search approach for optimal feature selection. Elephant optimization (EO) and Ant Colony optimization (ACO) algorithms coupled with Naïve Bayes (NB), Support Vector Machin (SVM), and J48 classifiers were used to highlight the optimization capability of meta-heuristic search for multi-dimensional feature selection problem in document categorization. In addition, the performance results for feature selection using the two meta-heuristic based approaches (EO and ACO) were compared with conventional Best First Search (BFS) and Greedy Stepwise (GS) algorithms on news document categorization. The comparative results showed that global optimal feature subsets were attained using adaptive parameters tuning in meta-heuristic based feature selection optimization scheme. In addition, the selected number of feature subsets were minimized dramatically for document classification.

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Comparative Study Between Two Swarm Intelligence Automatic Text Summaries

Cited by 2 publications

References 23 publications

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection

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