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

Xinnuo Xu, Ondřej Dušek, Shashi Narayan, Verena Rieser, Ioannis Konstas


Abstract
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 it’s 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.
Anthology ID:
2021.findings-emnlp.133
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1541–1552
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.133
DOI:
10.18653/v1/2021.findings-emnlp.133
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.133.pdf
Code
 xinnuoxu/miranews
Data
NEWSROOM