Extracting hypotheses and their supporting statistical evidence from full-text scientific articles is central to the synthesis of empirical findings, but remains difficult due to document length and the distribution of scientific arguments across sections of the paper. The work studies a sequential full-text extraction setting, where the statement of a primary finding in an article's abstract is linked to (i) a corresponding hypothesis statement in the paper body and (ii) the statistical evidence that supports or refutes that hypothesis. This formulation induces a challenging within-document retrieval setting in which many candidate paragraphs are topically related to the finding but differ in rhetorical role, creating hard negatives for retrieval and extraction. Using a two-stage retrieve-and-extract framework, we conduct a controlled study of retrieval design choices, varying context quantity, context quality (standard Retrieval Augmented Generation, reranking, and a fine-tuned retriever paired with reranking), as well as an oracle paragraph setting to separate retrieval failures from extraction limits across four Large Language Model extractors. We find that targeted context selection consistently improves hypothesis extraction relative to full-text prompting, with gains concentrated in configurations that optimize retrieval quality and context cleanliness. In contrast, statistical evidence extraction remains substantially harder. Even with oracle paragraphs, performance remains moderate, indicating persistent extractor limitations in handling hybrid numeric-textual statements rather than retrieval failures alone.

翻译：从全文学术论文中提取假设及其支撑统计证据，是整合实证发现的核心任务，但由于论文篇幅较长且科学论点分散于各章节，该任务仍具挑战性。本研究探讨了一种序贯式全文本提取场景：将论文摘要中的主要发现陈述与（i）正文中对应的假设陈述以及（ii）支持或反驳该假设的统计证据进行关联。该设定引出了一个极具挑战性的文档内检索问题——许多候选段落虽与发现主题相关，但修辞功能各异，从而形成了检索与提取过程中的困难负例。基于两阶段检索-提取框架，我们开展了针对检索设计选择的受控实验，系统比较了上下文数量、上下文质量（标准检索增强生成、重排序、微调检索器配合重排序），以及一种理想段落设置——通过四种大型语言模型提取器分离检索失败与提取能力缺陷。实验发现：相较于全文本提示，定向上下文选择能持续提升假设提取效果，其增益集中于优化检索质量与上下文纯净度的配置中。相比之下，统计证据提取仍具显著难度：即使在理想段落设置下，性能仍处于中等水平，这表明当前提取器在处理混合数值-文本陈述时存在持续性能力瓶颈，而非单纯的检索失败问题。