Multi-Document Summarization using CS-ABC Optimization Algorithm

Kumar, K. Suresh; Nagalla, Sudhakar

doi:10.4108/eai.13-7-2018.163835

Cited by 1 publication

(1 citation statement)

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“…K.K. Chandra et al, 2023 [34] followed a compelling report rundown framework applying SVM classifier methodology. For picking ideal sentence sets, the proposed procedure applies the crossbreed ABC-CS enhancement calculation.…”

Section: Selvanmentioning

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: Selvanmentioning

confidence: 99%