Stress Testing Factual Consistency Metrics for Long-Document Summarization
Recent advancements in natural language processing have brought forth significant improvements in text summarization techniques. However, evaluating the factual consistency of these summaries, particularly for long documents, remains a formidable challenge. The study presented in arXiv:2511.07689v2 delves into this issue by systematically assessing the efficacy of six widely utilized reference-free factuality metrics that were originally designed for short-form summarization.
Challenges in Long-Document Summarization
Summarizing long documents involves unique complexities, primarily stemming from input length limitations and long-range dependencies. Traditional metrics often falter in effectively capturing the nuances required for assessing factual consistency in lengthy texts. The researchers sought to address this gap by exploring the reliability of existing metrics under various perturbations.
Methodology
The study employed seven distinct factuality-preserving perturbations to evaluate the robustness of the selected metrics:
- Paraphrasing
- Simplification
- Synonym Replacement
- Logically Equivalent Negations
- Vocabulary Reduction
- Compression
- Source Text Insertion
These perturbations were designed to maintain factual integrity while challenging the metrics’ sensitivity to retrieval context and information density within claims. The researchers tested these methods across three long-form benchmark datasets, which encompassed diverse domains including science fiction, legal documentation, and scientific literature.
Key Findings
The results of the study revealed several critical insights regarding the performance of existing short-form metrics:
- Inconsistent scoring for semantically equivalent summaries.
- Declining reliability when evaluating information-dense claims that closely resemble multiple sections of the source document.
- Improved stability in certain domains when expanding the retrieval context; however, no metric consistently upheld factual alignment under long-context conditions.
These findings underscore the limitations of current metrics in effectively measuring factual consistency in long-document summarization, revealing a significant need for enhanced evaluation methods.
Future Directions for Improvement
In light of the challenges identified, the study proposes several concrete strategies for advancing factuality evaluation in long-form summarization:
- Implementing multi-span reasoning techniques to better capture complex relationships within the text.
- Developing context-aware calibration methods to adjust for varying levels of information density.
- Training metrics on variations that preserve meaning to bolster their robustness against perturbations.
By addressing these key areas, researchers aim to improve the reliability of factual consistency metrics, ultimately enhancing the quality and accuracy of long-document summarization.
For those interested in further exploring this research, the authors have made their code, perturbed data, and necessary scripts available for reproduction of results at GitHub.
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