PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment

Ge Luo; Hebi Li; Youbiao He; Forrest Bao

PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment

Ge Luo, Hebi Li, Youbiao He, Forrest Sheng Bao

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Abstract

Evaluating machine-generated summaries without a human-written reference summary has been a need for a long time. Inspired by preference labeling in existing work of summarization evaluation, we propose to judge summary quality by learning the preference rank of summaries using the Bradley-Terry power ranking model from inferior summaries generated by corrupting base summaries. Extensive experiments on several datasets show that our weakly supervised scheme can produce scores highly correlated with human ratings.

Anthology ID:: 2022.coling-1.515
Volume:: Proceedings of the 29th International Conference on Computational Linguistics
Month:: October
Year:: 2022
Address:: Gyeongju, Republic of Korea
Editors:: Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:: COLING
SIG:
Publisher:: International Committee on Computational Linguistics
Note:
Pages:: 5896–5903
Language:
URL:: https://aclanthology.org/2022.coling-1.515/
DOI:
Bibkey:
Cite (ACL):: Ge Luo, Hebi Li, Youbiao He, and Forrest Sheng Bao. 2022. PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5896–5903, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):: PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment (Luo et al., COLING 2022)
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PDF:: https://aclanthology.org/2022.coling-1.515.pdf

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@inproceedings{luo-etal-2022-prefscore,
    title = "{P}ref{S}core: Pairwise Preference Learning for Reference-free Summarization Quality Assessment",
    author = "Luo, Ge  and
      Li, Hebi  and
      He, Youbiao  and
      Bao, Forrest Sheng",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.515/",
    pages = "5896--5903",
    abstract = "Evaluating machine-generated summaries without a human-written reference summary has been a need for a long time. Inspired by preference labeling in existing work of summarization evaluation, we propose to judge summary quality by learning the preference rank of summaries using the Bradley-Terry power ranking model from inferior summaries generated by corrupting base summaries. Extensive experiments on several datasets show that our weakly supervised scheme can produce scores highly correlated with human ratings."
}

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%0 Conference Proceedings
%T PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment
%A Luo, Ge
%A Li, Hebi
%A He, Youbiao
%A Bao, Forrest Sheng
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F luo-etal-2022-prefscore
%X Evaluating machine-generated summaries without a human-written reference summary has been a need for a long time. Inspired by preference labeling in existing work of summarization evaluation, we propose to judge summary quality by learning the preference rank of summaries using the Bradley-Terry power ranking model from inferior summaries generated by corrupting base summaries. Extensive experiments on several datasets show that our weakly supervised scheme can produce scores highly correlated with human ratings.
%U https://aclanthology.org/2022.coling-1.515/
%P 5896-5903

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Markdown (Informal)

[PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment](https://aclanthology.org/2022.coling-1.515/) (Luo et al., COLING 2022)

PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment (Luo et al., COLING 2022)

ACL

Ge Luo, Hebi Li, Youbiao He, and Forrest Sheng Bao. 2022. PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5896–5903, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.