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Normalizing Section Names and Structure of Scientific Articles

The growing amount of scientific literature has increased the need for automatic methods that can retrieve, process, and exploit scholarly content. In this work, we explore section name normalization and hierarchy prediction for scientific articles using a two-level taxonomy. We compare independent, sequential classification models, and generative large language models on the SASC dataset. Results show that classification approaches, particularly sequential models that employ document-level context, consistently outperform generative methods. Incorporating section content is essential for fine-grained classification, while generative models remain limited in zero-shot settings. Our experiments highlight the importance of structure-aware modelling for large-scale scholarly document processing, and the importance of section normalization for the development of advanced research mapping and research assessment tools.