MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization

One of the most challenging aspects of current single-document news summarization is that the summary often contains extrinsic hallucinations, i.e., facts that are not present in the source document, which are often derived via world knowledge. This causes summarisation systems to act more like open-ended language models tending to hallucinate facts that are erroneous. In this paper, we mitigate this problem with the help of multiple supplementary resource documents assisting the task. We present a new dataset MiraNews and benchmark existing summarisation models. In contrast to multi-document summarization, which addresses multiple events from several source documents, we still aim at generating a summary for a single document. We show via data analysis that its not only the models which are to blame: more than 27 of facts mentioned in the gold summaries of MiraNews are better grounded on assisting documents than in the main source articles. An error analysis of generated summaries from pretrained models fine-tuned on MIRANEWS reveals that this has an even bigger effects on models: assisted summarisation reduces 55 of hallucinations when compared to single-document summarisation models trained on the main article only.