  This work addresses the challenge of factual consistency in text generation
systems. We unify the tasks of Natural Language Inference, Summarization
Evaluation, Factuality Verification and Factual Consistency Evaluation to train
models capable of evaluating the factual consistency of source-target pairs
across diverse domains. We rigorously evaluate these against eight baselines on
a comprehensive benchmark suite comprising 22 datasets that span various tasks,
domains, and document lengths. Results demonstrate that our method achieves
state-of-the-art performance on this heterogeneous benchmark while addressing
efficiency concerns and attaining cross-domain generalization.
