Evaluating the Factual Consistency of Abstractive Text Summarization
Evaluating the Factual Consistency of Abstractive Text Summarization
The most common metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and generated summaries. Training data is generated by applying a series of rule-based transformations …