Neural Text Summarization: A Critical Evaluation
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Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Research suggests that up to 30% of machine-generated abstractive summaries may contain factual inconsistencies.
“Research suggests that up to 30% of machine-generated abstractive summaries may contain factual inconsistencies [52, 53].”
From All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs? (2026) · cites this paper as [52]
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Lipton, Z. C., & Steinhardt, J. (2019) · Queue · doi:10.1145/3317287.3328534 · Cited by 231 · see citing works
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“Human judgments are considered the gold standard for evaluating model-generated sum-1 maries (Kryscinski et al., 2019; Fabbri et al., 2021) and generated text more broadly (Celikyilmaz et al., 2020).”
Background
- Authors
- Kryscinski, W., Keskar, N. S., McCann, B., Xiong, C., & Socher, R.
- Affiliations
- Salesforce (United States) (US)
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 540-551
- OpenAlex
- W2969286400
- Cited by
- 335
- References
- 3
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 21 · 2023: 73 · 2022: 59 · 2021: 88 · 2020: 56 · 2019: 6
- FWCI
- 19.99
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- abstractive summarization, extractive summarization, summarization evaluation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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