@inproceedings{bao-etal-2022-suenes,
title = "{S}ue{N}es: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling",
author = "Bao, Forrest and
Luo, Ge and
Li, Hebi and
Qiu, Minghui and
Yang, Yinfei and
He, Youbiao and
Chen, Cen",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.175/",
doi = "10.18653/v1/2022.naacl-main.175",
pages = "2450--2458",
abstract = "Canonical automatic summary evaluation metrics, such as ROUGE, focus on lexical similarity which cannot well capture semantics nor linguistic quality and require a reference summary which is costly to obtain. Recently, there have been a growing number of efforts to alleviate either or both of the two drawbacks. In this paper, we present a proof-of-concept study to a weakly supervised summary evaluation approach without the presence of reference summaries. Massive data in existing summarization datasets are transformed for training by pairing documents with corrupted reference summaries. In cross-domain tests, our strategy outperforms baselines with promising improvements, and show a great advantage in gauging linguistic qualities over all metrics."
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%0 Conference Proceedings
%T SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling
%A Bao, Forrest
%A Luo, Ge
%A Li, Hebi
%A Qiu, Minghui
%A Yang, Yinfei
%A He, Youbiao
%A Chen, Cen
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F bao-etal-2022-suenes
%X Canonical automatic summary evaluation metrics, such as ROUGE, focus on lexical similarity which cannot well capture semantics nor linguistic quality and require a reference summary which is costly to obtain. Recently, there have been a growing number of efforts to alleviate either or both of the two drawbacks. In this paper, we present a proof-of-concept study to a weakly supervised summary evaluation approach without the presence of reference summaries. Massive data in existing summarization datasets are transformed for training by pairing documents with corrupted reference summaries. In cross-domain tests, our strategy outperforms baselines with promising improvements, and show a great advantage in gauging linguistic qualities over all metrics.
%R 10.18653/v1/2022.naacl-main.175
%U https://aclanthology.org/2022.naacl-main.175/
%U https://doi.org/10.18653/v1/2022.naacl-main.175
%P 2450-2458
Markdown (Informal)
[SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling](https://aclanthology.org/2022.naacl-main.175/) (Bao et al., NAACL 2022)
ACL