@inproceedings{liu-etal-2023-towards-interpretable,
title = "Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation",
author = "Liu, Yixin and
Fabbri, Alexander and
Zhao, Yilun and
Liu, Pengfei and
Joty, Shafiq and
Wu, Chien-Sheng and
Xiong, Caiming and
Radev, Dragomir",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.1018",
doi = "10.18653/v1/2023.emnlp-main.1018",
pages = "16360--16368",
abstract = "Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. In this work, we develop strong-performing automatic metrics for reference-based summarization evaluation, based on a two-stage evaluation pipeline that first extracts basic information units from one text sequence and then checks the extracted units in another sequence. The metrics we developed include two-stage metrics that can provide high interpretability at both the fine-grained unit level and summary level, and one-stage metrics that achieve a balance between efficiency and interpretability. We make the developed tools publicly available at https://github.com/Yale-LILY/AutoACU.",
}
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<abstract>Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. In this work, we develop strong-performing automatic metrics for reference-based summarization evaluation, based on a two-stage evaluation pipeline that first extracts basic information units from one text sequence and then checks the extracted units in another sequence. The metrics we developed include two-stage metrics that can provide high interpretability at both the fine-grained unit level and summary level, and one-stage metrics that achieve a balance between efficiency and interpretability. We make the developed tools publicly available at https://github.com/Yale-LILY/AutoACU.</abstract>
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%0 Conference Proceedings
%T Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation
%A Liu, Yixin
%A Fabbri, Alexander
%A Zhao, Yilun
%A Liu, Pengfei
%A Joty, Shafiq
%A Wu, Chien-Sheng
%A Xiong, Caiming
%A Radev, Dragomir
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-towards-interpretable
%X Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. In this work, we develop strong-performing automatic metrics for reference-based summarization evaluation, based on a two-stage evaluation pipeline that first extracts basic information units from one text sequence and then checks the extracted units in another sequence. The metrics we developed include two-stage metrics that can provide high interpretability at both the fine-grained unit level and summary level, and one-stage metrics that achieve a balance between efficiency and interpretability. We make the developed tools publicly available at https://github.com/Yale-LILY/AutoACU.
%R 10.18653/v1/2023.emnlp-main.1018
%U https://aclanthology.org/2023.emnlp-main.1018
%U https://doi.org/10.18653/v1/2023.emnlp-main.1018
%P 16360-16368
Markdown (Informal)
[Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation](https://aclanthology.org/2023.emnlp-main.1018) (Liu et al., EMNLP 2023)
ACL