QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization (2112.08542v2)

Abstract: Factual consistency is an essential quality of text summarization models in practical settings. Existing work in evaluating this dimension can be broadly categorized into two lines of research, entailment-based and question answering (QA)-based metrics, and different experimental setups often lead to contrasting conclusions as to which paradigm performs the best. In this work, we conduct an extensive comparison of entailment and QA-based metrics, demonstrating that carefully choosing the components of a QA-based metric, especially question generation and answerability classification, is critical to performance. Building on those insights, we propose an optimized metric, which we call QAFactEval, that leads to a 14% average improvement over previous QA-based metrics on the SummaC factual consistency benchmark, and also outperforms the best-performing entailment-based metric. Moreover, we find that QA-based and entailment-based metrics can offer complementary signals and be combined into a single metric for a further performance boost.

QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization

The paper addresses the critical issue of factual consistency in text summarization, focusing on improving the evaluation metrics used to assess this attribute. The authors categorize existing approaches into entailment-based and question answering (QA)-based metrics, each with distinct advantages and limitations. They identify that different experimental setups can lead to contrasting conclusions about which metric performs best, necessitating a comprehensive comparative analysis.

The authors conduct an extensive comparison of these two paradigms and highlight that the choice of components in a QA-based metric, notably question generation and answerability classification, significantly impacts performance. Informed by this analysis, the authors propose a novel metric named QAFactEval, which optimizes QA-based evaluation by refining these components. Empirical results indicate a 14% average improvement over previous QA-based metrics on the SummaC factual consistency benchmark and superior performance compared to the best-performing entailment-based metric.

A key finding of the research underscores the potential for QA-based and entailment-based metrics to provide complementary signals. The authors suggest combining these approaches into a singular metric to achieve further improvements in performance, thus advocating for multi-faceted evaluation strategies in factual consistency assessment.

Implications and Future Directions

Practically, the development of QAFactEval has significant implications for the deployment of text summarization systems in real-world settings where factual accuracy is paramount, such as news summarization and legal document simplification. Theoretically, the paper contributes to the understanding of how different components and configurations of QA-based metrics influence performance, guiding future research in metric optimization.

This work also opens pathways for further exploration in hybrid approaches that leverage the strengths of various evaluation paradigms to enhance consistency checks. As natural language processing continues to evolve, integrating multifaceted evaluation strategies will likely become crucial in developing robust and reliable AI systems.

In future developments in AI, especially concerning LLMs and advanced summarization systems, methodologies like QAFactEval can play an instrumental role in ensuring reliable information dissemination. Integrating such metrics into training and validation pipelines can lead to the advancement of more reliable LLMs, with applications extending beyond summarization to other areas requiring factual consistency, including dialog systems and automated content generation.

The paper thus not only advances the technical implementation of factual consistency evaluation but also provides a foundational framework for ongoing research into robust AI-driven summarization solutions.