科学事实核查的下一阶段：从复杂结构化学术论文中进行高级证据检索
The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers
AI总结：
本文针对现有科学事实核查仅依赖摘要的局限，提出面向全文复杂结构化文献的高级证据检索框架，涵盖证据驱动检索、时间感知引文追踪、结构化解析、复杂表达处理及文献可信度评估，并通过初步实验验证其可行性。
摘要：
科学事实核查旨在通过检索和分析研究文献中的证据来判断科学声明的真实性。该问题本质上比一般事实核查更为复杂，因为它必须适应科学知识不断演变的特性、学术文献的结构复杂性以及长篇、多模态科学表达所带来的挑战。然而，现有方法聚焦于该问题的简化版本，基于由摘要而非全文组成的小规模数据集，从而回避了与处理完整文档相关的独特挑战。本文审视了当前科学事实核查系统的局限性，并揭示了可用于提升其性能的诸多潜在特征和资源。本文确定了证据检索中的关键研究挑战，包括：（1）证据驱动检索，以解决语义局限性和主题不平衡问题；（2）具有引文追踪的时间感知证据检索，以缓解信息过时问题；（3）结构化文档解析，以利用长距离上下文；（4）处理复杂科学表达，包括表格、图形和领域特定术语；（5）评估科学文献的可信度。本文进行了初步实验以证实这些挑战并确定潜在解决方案。这篇前瞻性论文旨在通过一个面向现实世界应用定制的专用信息检索（IR）系统来推进科学事实核查。
英文摘要：
Scientific fact-checking aims to determine the veracity of scientific claims by retrieving and analysing evidence from research literature. The problem is inherently more complex than general fact-checking since it must accommodate the evolving nature of scientific knowledge, the structural complexity of academic literature and the challenges posed by long-form, multimodal scientific expression. However, existing approaches focus on simplified versions of the problem based on small-scale datasets consisting of abstracts rather than full papers, thereby avoiding the distinct challenges associated with processing complete documents. This paper examines the limitations of current scientific fact-checking systems and reveals the many potential features and resources that could be exploited to advance their performance. It identifies key research challenges within evidence retrieval, including (1) evidence-driven retrieval that addresses semantic limitations and topic imbalance (2) time-aware evidence retrieval with citation tracking to mitigate outdated information, (3) structured document parsing to leverage long-range context, (4) handling complex scientific expressions, including tables, figures, and domain-specific terminology and (5) assessing the credibility of scientific literature. Preliminary experiments were conducted to substantiate these challenges and identify potential solutions. This perspective paper aims to advance scientific fact-checking with a specialised IR system tailored for real-world applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
