Abstract
Scientific information extraction (SciIE) has primarily relied on entity-relation extraction in narrow domains, limiting its applicability to interdisciplinary research and struggling to capture the necessary context of scientific information, often resulting in fragmented or conflicting statements. In this paper, we introduce SciEvent, a novel multi-domain benchmark of scientific abstracts annotated via a unified event extraction (EE) schema designed to enable structured and context-aware understanding of scientific content. It includes 500 abstracts across five research domains, with manual annotations of event segments, triggers, and fine-grained arguments. We define SciIE as a multi-stage EE pipeline: (1) segmenting abstracts into core scientific activities--Background, Method, Result, and Conclusion; and (2) extracting the corresponding triggers and arguments. Experiments with fine-tuned EE models, large language models (LLMs), and human annotators reveal a performance gap, with current models struggling in domains such as sociology and humanities. SciEvent serves as a challenging benchmark and a step toward generalizable, multi-domain SciIE.
Abstract (translated)
科学信息抽取（SciIE）主要依赖于狭窄领域的实体关系提取，这限制了其在跨学科研究中的适用性，并且难以捕捉到科学信息所需的背景语境，常常导致支离破碎或相互矛盾的表述。本文中，我们介绍了SciEvent，这是一个新的多领域基准测试集，通过统一的事件抽取（EE）模式对科学摘要进行了标注，旨在实现对科学内容的结构化和上下文感知的理解。该数据集包括了来自五个研究领域的500篇摘要，并对其事件片段、触发词及细粒度论点进行了手动注释。我们定义SciIE为一个多阶段的EE流水线：（1）将摘要分割为核心科学活动，如背景、方法、结果和结论；以及（2）提取相应的触发词和论点。 实验结果显示了通过微调事件抽取模型、大型语言模型（LLMs）及人工标注者之间的性能差距，在诸如社会学和人文学科等特定领域，当前的模型表现较差。SciEvent作为一个具有挑战性的基准测试集，并朝着通用性、多领域的SciIE迈进了一步。
URL
https://arxiv.org/abs/2509.15620
