Scholarly Information Extraction

Scholarly information extraction (SIE) aims to automatically extract key information from scientific literature, facilitating knowledge organization and discovery within the rapidly growing body of research. Current research focuses on improving the accuracy and efficiency of information extraction using large language models (LLMs), often fine-tuned with specialized datasets and employing techniques like prompt engineering and multimodal approaches. This work addresses challenges such as parsing diverse document structures (e.g., formulas, tables), identifying named entities (e.g., machine learning models, datasets), and detecting nuanced information like scientific uncertainty and complex mathematical definitions. The resulting advancements in SIE are crucial for building comprehensive knowledge graphs, improving search and retrieval systems, and accelerating scientific progress.