MoFE: Mixture of Factual Experts for Controlling Hallucinations in Abstractive Summarization

Prafulla Kumar Choubey, Jesse Vig, Wenhao Liu, Nazneen Rajani

Keywords: abstractive summarization, content hallucinations

Abstract: Neural abstractive summarization models are susceptible to generating factually inconsistent content, a phenomenon known as hallucination. This limits the usability and adoption of these systems in real-world applications. To reduce the presence of hallucination, we propose the Mixture of Factual Experts (MoFE) model, which combines multiple summarization experts that each target a specific type of error. We train our experts using reinforcement learning (RL) to minimize the error defined by two factual consistency metrics: entity overlap and dependency arc entailment. We construct MoFE by combining the experts using two ensembling strategies (weights and logits) and evaluate them on two summarization datasets (XSUM and CNN/DM). Our experiments on BART models show that the MoFE improves performance according to both entity overlap and dependency arc entailment, without a significant performance drop on standard ROUGE metrics. The performance improvement also transfers to unseen factual consistency metrics, such as question answer-based factuality evaluation metric and BERTScore precision with respect to the source document.

One-sentence Summary: We propose a simple framework that applies ensemble of factual experts, that each target different kind of factual errors, to reduce content hallucinations in abstractive summarization.