In this paper, we champion the use of structured and semantic content representation of discourse-based scholarly communication, inspired by tools like Wikipedia infoboxes or structured Amazon product descriptions. These representations provide users with a concise overview, aiding scientists in navigating the dense academic landscape. Our novel automated approach leverages the robust text generation capabilities of LLMs to produce structured scholarly contribution summaries, offering both a practical solution and insights into LLMs' emergent abilities. For LLMs, the prime focus is on improving their general intelligence as conversational agents. We argue that these models can also be applied effectively in information extraction (IE), specifically in complex IE tasks within terse domains like Science. This paradigm shift replaces the traditional modular, pipelined machine learning approach with a simpler objective expressed through instructions. Our results show that finetuned FLAN-T5 with 1000x fewer parameters than the state-of-the-art GPT-davinci is competitive for the task.

翻译：本文倡导基于篇章学术交流的结构化与语义化内容表征，借鉴维基百科信息框或亚马逊商品结构化描述等工具。此类表征可为用户提供精炼概览，助力科研人员应对信息密集的学术环境。我们提出一种创新自动化方法，利用大语言模型（LLMs）强大的文本生成能力，生成结构化学术贡献摘要，既提供实用解决方案，也揭示LLMs的涌现能力。当前对LLMs的研究主要聚焦于提升其作为对话助手的通用智能，本研究论证此类模型同样可有效应用于信息提取（IE）领域，尤其是在科学这类精炼领域的复杂IE任务中。这一范式转变将传统的模块化流水线机器学习方法，代之以通过指令表述的简化目标。实验结果表明，参数量仅为最先进GPT-davinci千分之一的微调FLAN-T5模型在该任务中展现出同等竞争力。