@inproceedings{luo-etal-2024-summacoz,
title = "{S}umma{C}oz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization",
author = "Luo, Ge and
Fan, Weisi and
Li, Miaoran and
Sun, Guoruizhe and
Zhang, Runlong and
Xu, Chenyu and
Bao, Forrest Sheng",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.210",
pages = "3689--3702",
abstract = "Summarization is an important application of Large Language Models (LLMs). When judging the quality of a summary, factual consistency holds a significant weight. Despite numerous efforts dedicated to building factual inconsistency detectors, the exploration of explanability remains limited among existing effort. In this study, we incorporate both human-annotated and model-generated natural language explanations elucidating how a summary deviates and thus becomes inconsistent with its source article. We build our explanation-augmented dataset on top of the widely used SummaC summarization consistency benchmark. Additionally, we develop an inconsistency detector that is jointly trained with the collected explanations. Our findings demonstrate that integrating explanations during training not only enables the model to provide rationales for its judgments but also enhances its accuracy significantly.",
}
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<abstract>Summarization is an important application of Large Language Models (LLMs). When judging the quality of a summary, factual consistency holds a significant weight. Despite numerous efforts dedicated to building factual inconsistency detectors, the exploration of explanability remains limited among existing effort. In this study, we incorporate both human-annotated and model-generated natural language explanations elucidating how a summary deviates and thus becomes inconsistent with its source article. We build our explanation-augmented dataset on top of the widely used SummaC summarization consistency benchmark. Additionally, we develop an inconsistency detector that is jointly trained with the collected explanations. Our findings demonstrate that integrating explanations during training not only enables the model to provide rationales for its judgments but also enhances its accuracy significantly.</abstract>
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%0 Conference Proceedings
%T SummaCoz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization
%A Luo, Ge
%A Fan, Weisi
%A Li, Miaoran
%A Sun, Guoruizhe
%A Zhang, Runlong
%A Xu, Chenyu
%A Bao, Forrest Sheng
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F luo-etal-2024-summacoz
%X Summarization is an important application of Large Language Models (LLMs). When judging the quality of a summary, factual consistency holds a significant weight. Despite numerous efforts dedicated to building factual inconsistency detectors, the exploration of explanability remains limited among existing effort. In this study, we incorporate both human-annotated and model-generated natural language explanations elucidating how a summary deviates and thus becomes inconsistent with its source article. We build our explanation-augmented dataset on top of the widely used SummaC summarization consistency benchmark. Additionally, we develop an inconsistency detector that is jointly trained with the collected explanations. Our findings demonstrate that integrating explanations during training not only enables the model to provide rationales for its judgments but also enhances its accuracy significantly.
%U https://aclanthology.org/2024.findings-emnlp.210
%P 3689-3702
Markdown (Informal)
[SummaCoz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization](https://aclanthology.org/2024.findings-emnlp.210) (Luo et al., Findings 2024)
ACL
- Ge Luo, Weisi Fan, Miaoran Li, Guoruizhe Sun, Runlong Zhang, Chenyu Xu, and Forrest Sheng Bao. 2024. SummaCoz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3689–3702, Miami, Florida, USA. Association for Computational Linguistics.