@inproceedings{chae-etal-2024-mitigating,
title = "Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information",
author = "Chae, Kyubyung and
Choi, Jaepill and
Jo, Yohan and
Kim, Taesup",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.117",
pages = "1809--1820",
abstract = "A primary challenge in abstractive summarization is hallucination{---}the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text. To alleviate this model bias, we introduce a decoding strategy based on domain-conditional pointwise mutual information. This strategy adjusts the generation probability of each token by comparing it with the token{'}s marginal probability within the domain of the source text. According to evaluation on the XSUM dataset, our method demonstrates improvement in terms of faithfulness and source relevance.",
}
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<abstract>A primary challenge in abstractive summarization is hallucination—the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text. To alleviate this model bias, we introduce a decoding strategy based on domain-conditional pointwise mutual information. This strategy adjusts the generation probability of each token by comparing it with the token’s marginal probability within the domain of the source text. According to evaluation on the XSUM dataset, our method demonstrates improvement in terms of faithfulness and source relevance.</abstract>
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%0 Conference Proceedings
%T Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information
%A Chae, Kyubyung
%A Choi, Jaepill
%A Jo, Yohan
%A Kim, Taesup
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F chae-etal-2024-mitigating
%X A primary challenge in abstractive summarization is hallucination—the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text. To alleviate this model bias, we introduce a decoding strategy based on domain-conditional pointwise mutual information. This strategy adjusts the generation probability of each token by comparing it with the token’s marginal probability within the domain of the source text. According to evaluation on the XSUM dataset, our method demonstrates improvement in terms of faithfulness and source relevance.
%U https://aclanthology.org/2024.findings-naacl.117
%P 1809-1820
Markdown (Informal)
[Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information](https://aclanthology.org/2024.findings-naacl.117) (Chae et al., Findings 2024)
ACL