Automated Scientific Article Generator System Based on GPT Algorithm

Automated Scientific Article Generator System Based on GPT Algorithm

Scientific Article Generation, GPT, Web Scraping, ROUGE, BERTScore

Composing scientific articles and synthesizing relevant literature are often time-consumingand challenging tasks for researchers. This studydeveloped an automated scientific article generator system that leverages advanced Artificial Intelligence capabilitiesto addressthese inefficiencies. The proposed system integrates the OpenAI API using the GPT algorithm to construct natural language generation with Web Scraping techniques, specifically targeting academic databases such as IEEE Xplore, to dynamically retrieve and incorporate up-to-datescholarly references. The system is designed to streamline the article writing process by generating cohesive, structured text based on user-definedtopics and seamlessly embedding pertinent citations. The performance of the generated articles was rigorously evaluated using quantitative metrics: ROUGE (for lexical overlap) and BERTScore (for semantic similarity) against reference texts. Empirical results are highly promising: the system achieved a BERTScore F1-Score of 84.46%, demonstrating superior semantic correspondence and contextual relevance while extracting critical information from source texts. This proposed technique can be apotentialsolutionto enhance writing efficiency and support academic documentation

I Wayan Budi Sentana1, Ni Putu Eka Apriyanthi2, Ni Nyoman Harini Puspita

https://ijicom.respati.ac.id/index.php/ijicom/article/view/189/123

International Journal of Informatics and Computation (IJICOM)

2025

Fajar bagus W

PDF

English

Text