@inproceedings{huang-etal-2023-swing,
title = "{SWING}: Balancing Coverage and Faithfulness for Dialogue Summarization",
author = "Huang, Kung-Hsiang and
Singh, Siffi and
Ma, Xiaofei and
Xiao, Wei and
Nan, Feng and
Dingwall, Nicholas and
Wang, William Yang and
McKeown, Kathleen",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.37/",
doi = "10.18653/v1/2023.findings-eacl.37",
pages = "512--525",
abstract = "Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries. To address this issue, we propose to utilize natural language inference (NLI) models to improve coverage while avoiding introducing factual inconsistencies. Specifically, we use NLI to compute fine-grained training signals to encourage the model to generate content in the reference summaries that have not been covered, as well as to distinguish between factually consistent and inconsistent generated sentences. Experiments on the DialogSum and SAMSum datasets confirm the effectiveness of the proposed approach in balancing coverage and faithfulness, validated with automatic metrics and human evaluations. Additionally, we compute the correlation between commonly used automatic metrics with human judgments in terms of three different dimensions regarding coverage and factual consistency to provide insight into the most suitable metric for evaluating dialogue summaries."
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<abstract>Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries. To address this issue, we propose to utilize natural language inference (NLI) models to improve coverage while avoiding introducing factual inconsistencies. Specifically, we use NLI to compute fine-grained training signals to encourage the model to generate content in the reference summaries that have not been covered, as well as to distinguish between factually consistent and inconsistent generated sentences. Experiments on the DialogSum and SAMSum datasets confirm the effectiveness of the proposed approach in balancing coverage and faithfulness, validated with automatic metrics and human evaluations. Additionally, we compute the correlation between commonly used automatic metrics with human judgments in terms of three different dimensions regarding coverage and factual consistency to provide insight into the most suitable metric for evaluating dialogue summaries.</abstract>
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%0 Conference Proceedings
%T SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
%A Huang, Kung-Hsiang
%A Singh, Siffi
%A Ma, Xiaofei
%A Xiao, Wei
%A Nan, Feng
%A Dingwall, Nicholas
%A Wang, William Yang
%A McKeown, Kathleen
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F huang-etal-2023-swing
%X Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries. To address this issue, we propose to utilize natural language inference (NLI) models to improve coverage while avoiding introducing factual inconsistencies. Specifically, we use NLI to compute fine-grained training signals to encourage the model to generate content in the reference summaries that have not been covered, as well as to distinguish between factually consistent and inconsistent generated sentences. Experiments on the DialogSum and SAMSum datasets confirm the effectiveness of the proposed approach in balancing coverage and faithfulness, validated with automatic metrics and human evaluations. Additionally, we compute the correlation between commonly used automatic metrics with human judgments in terms of three different dimensions regarding coverage and factual consistency to provide insight into the most suitable metric for evaluating dialogue summaries.
%R 10.18653/v1/2023.findings-eacl.37
%U https://aclanthology.org/2023.findings-eacl.37/
%U https://doi.org/10.18653/v1/2023.findings-eacl.37
%P 512-525
Markdown (Informal)
[SWING: Balancing Coverage and Faithfulness for Dialogue Summarization](https://aclanthology.org/2023.findings-eacl.37/) (Huang et al., Findings 2023)
ACL
- Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, and Kathleen McKeown. 2023. SWING: Balancing Coverage and Faithfulness for Dialogue Summarization. In Findings of the Association for Computational Linguistics: EACL 2023, pages 512–525, Dubrovnik, Croatia. Association for Computational Linguistics.