Multi-Fact Correction in Abstractive Text Summarization
This addresses the issue of generating incorrect facts in summaries for users relying on accurate information, representing an incremental improvement over existing methods.
The paper tackled the problem of factual inconsistency in abstractive text summarization by proposing Span-Fact, a suite of models that correct incorrect facts in summaries using knowledge from question answering models, resulting in significantly improved factual consistency without sacrificing summary quality.
Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE. However, system-generated abstractive summaries often face the pitfall of factual inconsistency: generating incorrect facts with respect to the source text. To address this challenge, we propose Span-Fact, a suite of two factual correction models that leverages knowledge learned from question answering models to make corrections in system-generated summaries via span selection. Our models employ single or multi-masking strategies to either iteratively or auto-regressively replace entities in order to ensure semantic consistency w.r.t. the source text, while retaining the syntactic structure of summaries generated by abstractive summarization models. Experiments show that our models significantly boost the factual consistency of system-generated summaries without sacrificing summary quality in terms of both automatic metrics and human evaluation.