Enhancing metaheuristic based extractive text summarization with fuzzy logic

Tomer, Minakshi; Kumar, Manoj; Hashmi, Adeel; Sharma, B.R.; Tomer, Uma

doi:10.1007/s00521-023-08209-5

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…The movement of particles in search space is predominantly determined by the individual best as well asthe global best position of particles in the space concerningthe place of the solution. By default, the particles tend to follow the global best and try to align their individual best with the global best position achieved in the iterations so far [41]. If other methods excluding spherical which are default method of CSO were used to evaluate the individual best and the global best scores of the particles then the progress of the particles in search space would be wholly different than what used to be in the regular implementation(spherical) and simulation of the algorithm.…”

Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

Single Document Text Summarization Based on the Modified Cat Swarm Optimization (Mcso) Algorithm

Rautaray,

Panigrahi,

Nayak

2024

Preprint

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In today's digital age, the World Wide Web has provided us with an immense volume of online information. Consequently, the challenge of extracting relevant insights from this vast data has emerged. Recently, text summarization has gained recognition as a solution for distilling valuable orderas of extensive credentials. Depending on the number of credentialsmeasured in favor of summarization is partitioned as single-document and multi-document summarization, which is a complex challenge for researchers in achieving accurate summaries. This research methodology, considered single document summarization by using the following steps they are text-pre-processing, Feature extraction, vectorization, and Modified Cat Swarm Optimization (MCSO) algorithm. Initially, the input documents are pre-processed for cleaning the data and they contain sentence segmentation, word tokenization, stop word removal, and lemmatization. In feature extraction, a score of the sentence is computed using Time Frequency-Inverse Domain Frequency (TF-IDF) also then words are formed toward a vector. After that, the generated vectors are post-processed, and Finally, the Modified Cat Swarm Optimization (MCSO) algorithm is utilized toprovideasummary of the single document with its fitness function. The performance of the model was compared with existing approaches such as Spherical, Rastrigin, and Griewank. To assess their effectiveness, we use benchmark datasets of the Document Understanding Conference (DUC) and evaluate algorithms using diverse metrics such as ROUGE score, F score, and summary accuracy, as well as best-case, worst-case, and average-case analyses. The experimental analysis demonstrates that Rosenbork surpasses other summarization methods included in the study.

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Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

Single Document Text Summarization Based on the Modified Cat Swarm Optimization (Mcso) Algorithm

Rautaray,

Panigrahi,

Nayak

2024

Preprint

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“…Among the various available metaheuristics, the combination of metaheuristics with fuzzy logic has gained significant attention in recent years. By integrating fuzzy logic with metaheuristics [21][22][23][24][25], a wide range of optimization problems and decision-making in complex and dynamic environments can be addressed. These hybrid techniques enable the effective capture and handling of vagueness and ambiguity in data, as well as adaptation to changes in problem conditions and user objectives.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Control Systems through Type-3 Fuzzy Logic Optimization

Ochoa,

Peraza,

Melin

et al. 2024

Mathematics

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The advancement of new tools in the field of control systems is a contemporary development. This work introduces the utilization of Type-3 fuzzy logic, a relatively recent concept that has been applied across various disciplines. In our case, a Type-3 fuzzy system is designed to enhance the optimization of parameters within the harmony search algorithm, specifically tailored for a control problem. Through a series of experiments, the efficacy of this novel Type-3 fuzzy logic tool is put to the test. Previous studies have primarily explored Type-1 and Type-2 fuzzy logic. To assess the performance of this new Type-3 fuzzy logic tool, a comparative analysis of results is conducted using statistical testing. The introduction of Type-3 fuzzy logic in the control domain represents a novel and innovative approach. This approach extends beyond the conventional Type-1 and Type-2 fuzzy logic, showcasing the dynamic evolution in the field. Results obtained through experimentation are analyzed, and statistical tests are employed to determine whether the Type-3 fuzzy logic tool yields superior outcomes compared to its predecessors. By doing so, this study contributes to the growing body of research that explores the potential benefits of Type-3 fuzzy logic and its application in control systems, offering new perspectives and opportunities for further advancements in the field. We have to mention that the utilization of Type-3 fuzzy logic in enhancing metaheuristics is a relatively new trend, and in this work, this research has extended this to the realm of harmony search. In addition, the application of the optimal design of the ball-and-beam fuzzy controllers has not been previously carried out with the Type-3 fuzzy harmony search algorithm, which is the novelty of this study.

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