Numerical Consistency Verification and Correction in Abstractive Summaries Using Semantic and Syntactic Features

We address numerical factuality errors in abstractive summaries and present three automatic correction methods: Key Segment Dependency-based Summary Reviser (KSDR), which aligns dependency heads of numbers after identifying key source segments; Source-Aligned Numerical Revision (SANR), which selects source sentences via sentence-level semantic similarity and matches corresponding numbers; and Meaning-Guided Numerical Reviser (MGNR), which leverages lexical and semantic features around numbers for flexible matching. Evaluated on 190 samples extracted from Tokyo Metropolitan Assembly minutes, KSDR achieves 83.7% accuracy and an F1-score of 0.810; SANR achieves 93.2% accuracy and an F1-score of 0.908, while MGNR attains 90.0% accuracy and 0.871 F1. In post-hoc summary revision, MGNR corrects 94.8% of the 58 summaries containing numerical errors, demonstrating practical utility for improving the reliability of political text summarization. These results suggest that incorporating semantic context around numbers yields more robust numerical error correction than relying solely on syntactic alignment.