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Entity-level Factual Consistency of Abstractive Text Summarization

Summary

Abstractive summarizers often hallucinate named entities, inserting people, places or organizations that never occur in the source article. ROUGE does not detect this kind of error. The authors address it with three entity-level metrics computed with spaCy NER and lenient, case-insensitive n-gram matching:

  • prec_s measures how many summary entities can be found in the source.
  • prec_t and recall_t (combined as F1_t) compare the generated entities with those in the reference summary.

Applying prec_s to the reference summaries of Newsroom, CNN/DailyMail and XSUM shows that the data itself is noisy, and XSUM is the worst case. The authors therefore filter the training data, removing reference sentences whose entities are absent from the document. They also test two training variants on top of BART-large (Fairseq):

  • Multi-task: an encoder-side BIO tagger that marks summary-worthy entities.
  • JAENS: the decoder first emits the salient entities and then writes the summary.

Filtering alone sharply reduces hallucination while keeping ROUGE largely intact. Both variants further raise entity precision and recall. This gives a cheap, reproducible way to measure and improve factuality at the entity level.

Key points

  • Reference-summary prec_s on the test sets is about 90.5% for Newsroom, 96.6% for CNNDM and only 79.3% for XSUM. The low XSUM figure arises because XSUM's summary is the article's lead sentence, which is removed from the source.
  • Filtering shrinks the XSUM training set from 203,540 to 135,155 pairs (roughly a third removed). Newsroom and CNNDM lose far less.
  • On XSUM, training on filtered data lifts model prec_s from 93.6% to 98.2%.
  • The multi-task loss is the MLE loss plus α times the BIO loss, with α between 0.1 and 0.5. JAENS beats multi-task on CNNDM and Newsroom but can lower ROUGE.
  • A manual check of 10 XSUM validation examples found that both the NER output and the entity matching were correct. Code is released at github.com/amazon-research/fact-check-summarization.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Named entity recognition

Cited by

90 papers of our library cite it (of the 140884 whose bibliographies we read); 9 of them have a page here, 81 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.eacl-main.235

@inproceedings{nan2021entity,
  title = {{Entity-level Factual Consistency of Abstractive Text Summarization}},
  author = {Nan, Feng and Nallapati, Ramesh and Wang, Zhiguo and Santos, Cicero Nogueira dos and Zhu, Henghui and Zhang, Dejiao and McKeown, Kathleen and Xiang, Bing},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  year = {2021},
  doi = {10.18653/v1/2021.eacl-main.235},
  url = {https://doi.org/10.18653/v1/2021.eacl-main.235}
}

References

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