mFACE: Multilingual Summarization with Factual Consistency Evaluation Article Swipe
YOU?
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· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.10622
ive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application. Several recent efforts attempt to address this by devising models that automatically detect factual inconsistencies in machine generated summaries. However, they focus exclusively on English, a language with abundant resources. In this work, we leverage factual consistency evaluation models to improve multilingual summarization. We explore two intuitive approaches to mitigate hallucinations based on the signal provided by a multilingual NLI model, namely data filtering and controlled generation. Experimental results in the 45 languages from the XLSum dataset show gains over strong baselines in both automatic and human evaluation.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2212.10622
- https://arxiv.org/pdf/2212.10622
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312107301
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4312107301Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2212.10622Digital Object Identifier
- Title
-
mFACE: Multilingual Summarization with Factual Consistency EvaluationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-20Full publication date if available
- Authors
-
Roee Aharoni, Shashi Narayan, Joshua Maynez, Jonathan Herzig, Elizabeth A. Clark, Mirella LapataList of authors in order
- Landing page
-
https://arxiv.org/abs/2212.10622Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2212.10622Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2212.10622Direct OA link when available
- Concepts
-
Automatic summarization, Computer science, Leverage (statistics), Consistency (knowledge bases), Natural language processing, Artificial intelligence, Focus (optics), Information retrieval, Machine learning, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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