@inproceedings{zeng-etal-2023-improving,
title = "Improving Consistency for Text Summarization with Energy Functions",
author = "Zeng, Qi and
Yin, Qingyu and
Li, Zheng and
Gao, Yifan and
Nag, Sreyashi and
Wang, Zhengyang and
Yin, Bing and
Ji, Heng and
Zhang, Chao",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.798",
doi = "10.18653/v1/2023.findings-emnlp.798",
pages = "11925--11931",
abstract = "Current abstractive summarization models often generate inconsistent content, i.e. texts that are not directly inferable from the source document, are not consistent with respect to world knowledge, or are self-contradictory. These inconsistencies motivate a new consistency taxonomy that we define as faithfulness, factuality, and self-supportiveness. However, most recent work on reducing inconsistency in document summarization only focuses on faithfulness detection and correction while ignoring other inconsistency phenomena, which limits the model{'}s scalability. To improve the general consistency we introduce EnergySum, where we apply the Residual Energy-based Model by designing energy scorers that reflect each type of consistency. These energy scores are utilized in candidate re-ranking during the sampling process. Experiments on XSUM and CNN/DM datasets show that EnergySum mitigates the trade-off between accuracy and consistency.",
}
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<abstract>Current abstractive summarization models often generate inconsistent content, i.e. texts that are not directly inferable from the source document, are not consistent with respect to world knowledge, or are self-contradictory. These inconsistencies motivate a new consistency taxonomy that we define as faithfulness, factuality, and self-supportiveness. However, most recent work on reducing inconsistency in document summarization only focuses on faithfulness detection and correction while ignoring other inconsistency phenomena, which limits the model’s scalability. To improve the general consistency we introduce EnergySum, where we apply the Residual Energy-based Model by designing energy scorers that reflect each type of consistency. These energy scores are utilized in candidate re-ranking during the sampling process. Experiments on XSUM and CNN/DM datasets show that EnergySum mitigates the trade-off between accuracy and consistency.</abstract>
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%0 Conference Proceedings
%T Improving Consistency for Text Summarization with Energy Functions
%A Zeng, Qi
%A Yin, Qingyu
%A Li, Zheng
%A Gao, Yifan
%A Nag, Sreyashi
%A Wang, Zhengyang
%A Yin, Bing
%A Ji, Heng
%A Zhang, Chao
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zeng-etal-2023-improving
%X Current abstractive summarization models often generate inconsistent content, i.e. texts that are not directly inferable from the source document, are not consistent with respect to world knowledge, or are self-contradictory. These inconsistencies motivate a new consistency taxonomy that we define as faithfulness, factuality, and self-supportiveness. However, most recent work on reducing inconsistency in document summarization only focuses on faithfulness detection and correction while ignoring other inconsistency phenomena, which limits the model’s scalability. To improve the general consistency we introduce EnergySum, where we apply the Residual Energy-based Model by designing energy scorers that reflect each type of consistency. These energy scores are utilized in candidate re-ranking during the sampling process. Experiments on XSUM and CNN/DM datasets show that EnergySum mitigates the trade-off between accuracy and consistency.
%R 10.18653/v1/2023.findings-emnlp.798
%U https://aclanthology.org/2023.findings-emnlp.798
%U https://doi.org/10.18653/v1/2023.findings-emnlp.798
%P 11925-11931
Markdown (Informal)
[Improving Consistency for Text Summarization with Energy Functions](https://aclanthology.org/2023.findings-emnlp.798) (Zeng et al., Findings 2023)
ACL
- Qi Zeng, Qingyu Yin, Zheng Li, Yifan Gao, Sreyashi Nag, Zhengyang Wang, Bing Yin, Heng Ji, and Chao Zhang. 2023. Improving Consistency for Text Summarization with Energy Functions. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11925–11931, Singapore. Association for Computational Linguistics.