| Abstract: | This thesis presents a novel microservices-based SaaS platform for the automated anal-ysis
of scientific paper introductions. The platform utilizes advanced natural language processing
(NLP) techniques to accurately cassify sentences within introductions ac-cording to their
rhetorical purpose, following the widely recognized IMRaD (Introduc-tion, Methods, Results,
and Discussion) structure.
The research involved a multi-stage process of developing and refining AI
models,culminating in a custom-built dataset and highly accurate BERT models fine-tuned for
specific IMRaD classification tasks. The platform's robust microservices architec-ture,leveraging
technologies such as Next.js, Spring Boot, Python,TensorFlow Serving,Express.js, Redis, and
Nginx, ensures scalability, maintainability, and optimal perfor-mance.
This platform provides users with an intuitive interface for uploading research pa-pers,
analyzing introductions, visualizing results, and offering valuable feedback. Pre-mium features,
including automated summarization and a hypothetical reconstruction of the author's thought
process, are available through a flexible subscription model.
This thesis demonstrates the feasibility and effectiveness of using AI to analyze IMRaD structure,
offering a valuable too for students, researchers, and educators to improve their understanding and
practice of scientific writing. The platform's modular design and open-source codebase lay a strong
foundation for future development and expansion in the field of scientific communication. |
