Jingnong Qu

AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation

Ensuring factual consistency is crucial for natural language generation tasks, particularly in abstractive summarization, where preserving the integrity of information is paramount. Prior works on evaluating factual consistency of summarization often take the entailment-based approaches that first g…