ROEC: Record-Oriented Evidence Construction for Document-Level Structured Information Extraction

Targeted structured information extraction organizes information according to a predefined schema. Extending this setting to the document level is challenging because target information is often sparse and distributed across the document, while non-target content can interfere with extraction. Beyond recovering relevant information, extracted outputs must satisfy predefined requirements and remain supported by the source text. We propose Record-Oriented Evidence Construction (ROEC), which replaces one-step record extraction with Two-Stage Structured Information Extraction. ROEC first extracts core fields and then completes the associated contextual fields, while retaining supporting source sentences throughout extraction. It further incorporates Record-Level Refinement to improve record granularity and conformity with task requirements and reduce redundancy, and Claim Verification and Support Scoring to evaluate extracted claims against retained supporting source sentences and derive a score reflecting the degree of source-text support. Experiments on agricultural research papers show that ROEC improves Relaxed and Strict F1 by 11.83 and 5.86 points over the strongest evaluated baseline under each criterion. The two-stage configuration improves recall and F1 over one-step extraction, while Record-Level Refinement further reduces false positives and increases true positives. Human assessment indicates that the support score reflects differences in source-text support and helps prioritize outputs for manual review of source-support errors.