FactWeaver: Atomic Fact Graph-Guided Non-Factoid Question Answering over Long Documents

Non-factoid questions seek explanations, comparisons, or assessments beyond a single fact. Answering them over long documents requires identifying dispersed facts relevant to a broad analytical question, determining their relationships, and integrating them into a coherent analysis. We introduce FactWeaver, an atomic fact graph-guided framework for non-factoid question answering over long documents. FactWeaver organizes question-relevant atomic facts under analytical perspectives and uses the retrieved context to establish candidate sequence, support, and conflict relations. The resulting atomic fact graph guides multiple agents in constructing candidate reasoning paths and auditing them for defects. Revision requirements are recorded in a shared state and guide answer generation. To evaluate the effectiveness of the framework, we construct LongNFQA, a benchmark comprising 624 non-factoid questions over 52 long documents totaling 6,048 pages. Human-reviewed evidence supports retrieval evaluation, while a human-derived multidimensional rubric and an automated judge validated against held-out human judgments support answer-quality assessment. On LongNFQA, FactWeaver improves Perspective Satisfaction@20 by 0.1384 and the Avg. answer-quality score by 0.5203 points over the strongest baseline. Ablation studies support the contributions of both atomic fact graph construction and graph-guided synthesis.