While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice. In this paper, we evaluate summaries produced by state-of-the-art models via crowdsourcing and show that such errors occur frequently, in particular with more abstractive models. We study whether textual entailment predictions can be used to detect such errors and if they can be reduced by reranking alternative predicted summaries. That leads to an interesting downstream application for entailment models. In our experiments, we find that outof-the-box entailment models trained on NLI datasets do not yet offer the desired performance for the downstream task and we therefore release our annotations as additional test data for future extrinsic evaluations of NLI.
REF studied whether existing natural language inference systems can be used to evaluate the factual correctness of generated summaries, and found models trained on existing datasets to be inadequate for this task.
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
ACL
Computer Science