Text Summarization for Multi Documents Using Genetic Algorithm

Nedunchelian, R.; Muthucumaraswamy, R.; Saranathan, E.

doi:10.3923/ijscomp.2012.20.23

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…Thus, optimization algorithms can be used to perform the creation of multiple-document summaries. Among these, we can highlight Genetic Algorithms (GA) (Neduncheli et al, 2012), Differential Evolution (DE) (Mishra et al, 2021), and Particle Swarm Optimization (PSO), among others (Rautray & Balabantaray, 2017).…”

Section: Synthesismentioning

confidence: 99%

A roadmap toward the automatic composition of systematic literature reviews

Júnior

Dutra

2021

Iberoamerican Journal of Science Measurement and Communication

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Objective. This paper presents an overview of existing artificial intelligence tools to produce systematic literature reviews. Furthermore, we propose a general framework resulting from combining these techniques to highlight the challenges and possibilities currently existing in this research area. Design/Methodology/Approach. We undertook a scoping review on the systematic literature review steps to automate them via computational techniques. Results/Discussion. The process of creating a literature review is both creative and technical. The technical part of this process is liable to automation. Based on the literature, we chose to divide this technical part into four steps: searching, screening, extraction, and synthesis. For each one of these steps, we presented practical artificial intelligence techniques to carry them out. In addition, we presented the obstacles encountered in the application of each technique. Conclusion. We proposed a framework for automatically creating systematic literature reviews by combining and placing existing techniques in stages where they possess the greatest potential to be useful. Despite still lacking practical assessment in different areas of knowledge, this proposal indicates ways with the potential to reduce the time-consuming and repetitive work embedded in the systematic literature review process. Originality/Value. The paper presents the current possibilities for automating systematic literature reviews and how they can work together to reduce researchers’ operational workload.

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Section: Synthesismentioning

confidence: 99%