@inproceedings{nan-etal-2021-entity,
title = "Entity-level Factual Consistency of Abstractive Text Summarization",
author = "Nan, Feng and
Nallapati, Ramesh and
Wang, Zhiguo and
Nogueira dos Santos, Cicero and
Zhu, Henghui and
Zhang, Dejiao and
McKeown, Kathleen and
Xiang, Bing",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.235",
doi = "10.18653/v1/2021.eacl-main.235",
pages = "2727--2733",
abstract = "A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data. In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.",
}
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<abstract>A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data. In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.</abstract>
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%0 Conference Proceedings
%T Entity-level Factual Consistency of Abstractive Text Summarization
%A Nan, Feng
%A Nallapati, Ramesh
%A Wang, Zhiguo
%A Nogueira dos Santos, Cicero
%A Zhu, Henghui
%A Zhang, Dejiao
%A McKeown, Kathleen
%A Xiang, Bing
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F nan-etal-2021-entity
%X A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data. In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.
%R 10.18653/v1/2021.eacl-main.235
%U https://aclanthology.org/2021.eacl-main.235
%U https://doi.org/10.18653/v1/2021.eacl-main.235
%P 2727-2733
Markdown (Informal)
[Entity-level Factual Consistency of Abstractive Text Summarization](https://aclanthology.org/2021.eacl-main.235) (Nan et al., EACL 2021)
ACL
- Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, and Bing Xiang. 2021. Entity-level Factual Consistency of Abstractive Text Summarization. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2727–2733, Online. Association for Computational Linguistics.