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.

本文介绍了一种基于语义内容表示的学术交流结构化方法，利用 LMMs 的文本生成能力，提供了结构化学术贡献摘要的自动化方法，并在信息提取方面展示了竞争力。