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In this paper, we champion the use of structured and semantic contentrepresentation of discourse-based scholarly communication, inspired by toolslike Wikipedia infoboxes or structured Amazon product descriptions. Theserepresentations provide users with a concise overview, aiding scientists innavigating the dense academic landscape. Our novel automated approach leveragesthe robust text generation capabilities of LLMs to produce structured scholarlycontribution summaries, offering both a practical solution and insights intoLLMs' emergent abilities.  For LLMs, the prime focus is on improving their general intelligence asconversational agents. We argue that these models can also be appliedeffectively in information extraction (IE), specifically in complex IE taskswithin terse domains like Science. This paradigm shift replaces the traditionalmodular, pipelined machine learning approach with a simpler objective expressedthrough instructions. Our results show that finetuned FLAN-T5 with 1000x fewerparameters than the state-of-the-art GPT-davinci is competitive for the task.

Large Language Models for Scientific Information Extraction: An Empirical Study for Virology