Improving Publishing: Extracting Keywords and Clustering Topics - Soekamto, Yosua Setyawan; Maryati, Indra; Christian, Christian; Kurniawan, Edwin (Bright Publisher, 2024)
Humans, by nature, are inclined to share knowledge across various platforms, such as educational institutions, media outlets, and specialized research publications like journals and conferences. The consistent oversight and evaluation of these publications by ranking bodies serve to maintain the integrity and quality of scholarly discourse on a global scale. However, there has been a decline in the proliferation of such publications in recent times, partly attributed to ethical misconduct within specific segments of the scholarly community. Despite implementing systems such as the Open Journal System (OJS), publishers grapple with the formidable task of managing editorial and review processes. Compounding the multifaceted nature of scholarly content, manual review procedures often lead to considerable time investment. Thus, a pressing need exists for advanced technological solutions to streamline the article selection process, empowering publishers to prioritize articles for review based on topical relevance. This study advocates adopting a comprehensive framework integrating advanced text analysis techniques such as keyword extraction, topic clustering, and summarization algorithms. These tools can be implemented and integrated by connecting with the database of the existing system. By leveraging these tools with the expertise of editorial and review teams, publishers can significantly expedite the initial assessment of submitted articles. Given the rapid technological advancements, publishers must embrace robust systems that enhance efficiency and effectiveness, particularly in reviewer assignments and article prioritization. This research employs the neural network approach of BERT and K-Means clustering to perform keyword extraction and topic clustering. Furthermore, using BERT facilitates accurate semantic understanding and context-aware representation of textual data. Additionally, BERT's pre-trained models enable its fine-tuning capability to allow customization to specific domains or tasks. By harnessing the power of BERT, publishers can gain deeper insights into the content of scholarly articles, leading to more informed decision-making and improved publication outcomes.