Text generation models can generate factually inconsistent text containing distorted or fabricated facts about the source text. Recent work has focused on building evaluation models to verify the factual correctness of semantically constrained text generation tasks such as document summarization. While the field of factuality evaluation is growing fast, we don't have well-defined criteria for measuring the effectiveness, generalizability, reliability, or sensitivity of the factuality metrics. Focusing on these aspects, in this paper, we introduce  a meta-evaluation framework for evaluating factual consistency metrics. We introduce five necessary , common-sense conditions for effective factuality metrics and experiment with nine recent factuality metrics using synthetic and human-labeled factuality data from short news, long news and dialogue summarization domains. Our framework enables assessing the efficiency of any new factual consistency metric on a variety of dimensions over multiple summarization domains and can be easily extended with new meta-evaluation criteria. We also present our conclusions towards standardizing the factuality evaluation metrics .
While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question. This major discrepancy between the surface-level fluency and the content-level correctness of neural generation has motivated a new line of research that seeks automatic metrics for evaluating the factuality of machine text. In this paper, we introduce GO FIGURE, a meta-evaluation framework for evaluating factuality evaluation metrics. We propose five necessary and intuitive conditions to evaluate factuality metrics on diagnostic factuality data across three different summarization tasks. Our benchmark analysis on ten factuality metrics reveals that our meta-evaluation framework provides a robust and efficient evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated .
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Text generation
While neural language
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factually inconsistent text containing distorted or fabricated facts about the source text. Recent work has focused on building evaluation models to verify the factual correctness of semantically constrained text generation tasks such as document summarization. While the field of factuality evaluation is growing fast, we don't have well-defined criteria for measuring the effectiveness, generalizability, reliability, or sensitivity of the factuality metrics. Focusing on these aspects, in
text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question. This major discrepancy between the surface-level fluency and the content-level correctness of neural generation has motivated a new line of research that seeks automatic metrics for evaluating the factuality of machine text. In
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GO FIGURE,
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factual consistency
factuality evaluation
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introduce five necessary , common-sense conditions for effective factuality metrics and experiment with nine recent factuality metrics using synthetic and human-labeled factuality data from short news, long news and dialogue summarization domains. Our framework enables assessing the efficiency of any new factual consistency metric on a variety of dimensions over multiple summarization domains and can be easily extended with new meta-evaluation criteria. We also present our conclusions towards standardizing the factuality evaluation metrics
propose five necessary and intuitive conditions to evaluate factuality metrics on diagnostic factuality data across three different summarization tasks. Our benchmark analysis on ten factuality metrics reveals that our meta-evaluation framework provides a robust and efficient evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated