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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.515.pdf
- Code
- nkwbtb/prefscore
- Data
- BigPatent, BillSum, NEWSROOM
Export citation
@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
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.