The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
Open accessAbstract
Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
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“Current literature on detecting and mitigating factual errors generated by machine learning models focuses predominantly on a single specific task, for example, retrieval-augmented verification models for QA (Lewis et al., 2020), hallucination detection models for text summarization (Fabbri et al., 2022), and execution-based evaluation for code (Shi et al., 2022).”
From FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios (2023) · cites this paper as Fabbri et al., 2022
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“Building on this foundational work, Fabbri et al. [80] conducted an in-depth evaluation of the components within QA-based metrics, yielding further enhancements in faithfulness evaluation.”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [80]
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- Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2020. FFCI: A framework for interpretable auto- matic evaluation of summarization. ArXiv preprint, abs/2011.13662. · arXiv
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LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
Krishna, K., Bransom, E., Kuehl, B., Iyyer, M., Dasigi, P., Cohan, A., & Lo, K. (2023) · arXiv · doi:10.48550/arxiv.2301.13298 · Cited by 3
“A large body of recent work has focused on new automatic evaluation methods for summarization via NLI-based algorithms (Falke et al., 2019; La-ban et al., 2022) or QA-based algorithms (Wang et al., 2020; Fabbri et al., 2022).”
Method
Metadata
- Authors
- Fabbri, A., Wu, C.-S., Liu, W., & Xiong, C.
- Affiliations
- Salesforce (United States) (US)
- Venue
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2022
- Pages
- 2587-2601
- OpenAlex
- W4226118367
- Cited by
- 119
- References
- 50
- Citations by year
- 2026: 30 · 2025: 21 · 2024: 14 · 2023: 48 · 2022: 6
- FWCI
- 12.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- question answering, summarization, factual accuracy, abstractive summarization
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