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Cited by 121Topic ModelingAdvanced Text Analysis TechniquesNatural Language Processing Techniques

Answers Unite! Unsupervised Metrics for Reinforced Summarization Models

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Scialom, Thomas, Lamprier, Sylvain, Piwowarski, Benjamin, Staiano, Jacopo·2019·Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
DOI 10.18653/v1/d19-1320
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Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano. 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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  • The QA confidence of answerability measure reflects answerability independently of how the answer is expressed, but does not account for possible model hallucinations, so it is only applied for the Recall-based component.

    “This measure reflects the answerability independently of the way the answer is expressed, but does not take into account possible model hallucinations, and it is therefore only applied for the Recall-based component.”

    From Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021) · cites this paper as Scialom et al., 2019

    Section 2.2.2 defines QA confidence as the QA model’s probability of the true answer and says QA-based metrics can find answers despite variability in the generated text. It does not say that QA confidence fails to account for model hallucinations or that it is applied only to a Recall-based component; searches for hallucinations, precision, and recall did not establish those points. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • To overcome low token-level F1 scores for semantically identical but lexically different answers, Scialom et al. use a measure of QA confidence of answerability, the complement of the probability that the QA model gives to the no-answer prediction.

    “To overcome the low scores assigned by the tokenlevel F1 measure to semantically-identical answers that are lexically different, they use a measure of the QA confidence of answerability (Scialom et al., 2019), which is the complement of the probability that the QA model gives to the”

    From Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021) · cites this paper as Scialom et al., 2019

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

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Research works printed but not linked: 28

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Cited by

Sorted by citations

Authors
Scialom, T., Lamprier, S., Piwowarski, B., & Staiano, J.
Affiliations
Centre National de la Recherche Scientifique (FR)
ORCID
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)
Publisher
Association for Computational Linguistics
Published
2019
Pages
3244-3254
DOI
10.18653/v1/d19-1320
OpenAlex
W2970892365
Cited by
121
References
2
Citations by year
2026: 4 · 2025: 3 · 2024: 13 · 2023: 33 · 2022: 21 · 2021: 34 · 2020: 13
FWCI
6.50
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
  • HAL (submittedVersion, other-oa)
  • Iris (University of Trento) (submittedVersion, cc-by-nc-sa)
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
unsupervised evaluation, reinforcement learning, summarization, abstractive summarization, summarization models, evaluation metrics

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