A weakly-supervised, model-based approach is provided for verifying or checking factual consistency and identifying conflicts between source documents and a generated summary. In some embodiments, an artificially generated training dataset is created by applying rule-based transformations to sentences sampled from one or more unannotated source documents of a dataset. Each of the resulting transformed sentences can be either semantically variant or invariant from the respective original sampled sentence, and labeled accordingly. In some embodiments, the generated training dataset is used to train a factual consistency checking model. The factual consistency checking model can classify whether a corresponding text summary is factually consistent with a source text document, and if so, may identify a span in the source text document that supports the corresponding text summary.
Evaluating the Factual Consistency of Abstractive Text Summarization