Scientific Document Representation

Scientific document representation focuses on creating effective computational models that capture the meaning and relationships within scientific publications, aiming to improve information retrieval, summarization, and knowledge discovery. Current research emphasizes multilingual support, leveraging transformer-based architectures like SPECTER and T5, and exploring contrastive learning techniques to refine similarity measures between documents, often incorporating citation networks. These advancements are crucial for enhancing scientific reproducibility, accelerating literature reviews, and facilitating more efficient knowledge synthesis across diverse scientific domains.