@inproceedings{mao-etal-2020-facet,
title = "Facet-Aware Evaluation for Extractive Summarization",
author = "Mao, Yuning and
Liu, Liyuan and
Zhu, Qi and
Ren, Xiang and
Han, Jiawei",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.445",
doi = "10.18653/v1/2020.acl-main.445",
pages = "4941--4957",
abstract = "Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries. Specifically, we treat each sentence in the reference summary as a \textit{facet}, identify the sentences in the document that express the semantics of each facet as \textit{support sentences} of the facet, and automatically evaluate extractive summarization methods by comparing the indices of extracted sentences and support sentences of all the facets in the reference summary. To facilitate this new evaluation setup, we construct an extractive version of the CNN/Daily Mail dataset and perform a thorough quantitative investigation, through which we demonstrate that facet-aware evaluation manifests better correlation with human judgment than ROUGE, enables fine-grained evaluation as well as comparative analysis, and reveals valuable insights of state-of-the-art summarization methods. Data can be found at \url{https://github.com/morningmoni/FAR}.",
}
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<abstract>Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries. Specifically, we treat each sentence in the reference summary as a facet, identify the sentences in the document that express the semantics of each facet as support sentences of the facet, and automatically evaluate extractive summarization methods by comparing the indices of extracted sentences and support sentences of all the facets in the reference summary. To facilitate this new evaluation setup, we construct an extractive version of the CNN/Daily Mail dataset and perform a thorough quantitative investigation, through which we demonstrate that facet-aware evaluation manifests better correlation with human judgment than ROUGE, enables fine-grained evaluation as well as comparative analysis, and reveals valuable insights of state-of-the-art summarization methods. Data can be found at https://github.com/morningmoni/FAR.</abstract>
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%0 Conference Proceedings
%T Facet-Aware Evaluation for Extractive Summarization
%A Mao, Yuning
%A Liu, Liyuan
%A Zhu, Qi
%A Ren, Xiang
%A Han, Jiawei
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F mao-etal-2020-facet
%X Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries. Specifically, we treat each sentence in the reference summary as a facet, identify the sentences in the document that express the semantics of each facet as support sentences of the facet, and automatically evaluate extractive summarization methods by comparing the indices of extracted sentences and support sentences of all the facets in the reference summary. To facilitate this new evaluation setup, we construct an extractive version of the CNN/Daily Mail dataset and perform a thorough quantitative investigation, through which we demonstrate that facet-aware evaluation manifests better correlation with human judgment than ROUGE, enables fine-grained evaluation as well as comparative analysis, and reveals valuable insights of state-of-the-art summarization methods. Data can be found at https://github.com/morningmoni/FAR.
%R 10.18653/v1/2020.acl-main.445
%U https://aclanthology.org/2020.acl-main.445
%U https://doi.org/10.18653/v1/2020.acl-main.445
%P 4941-4957
Markdown (Informal)
[Facet-Aware Evaluation for Extractive Summarization](https://aclanthology.org/2020.acl-main.445) (Mao et al., ACL 2020)
ACL
- Yuning Mao, Liyuan Liu, Qi Zhu, Xiang Ren, and Jiawei Han. 2020. Facet-Aware Evaluation for Extractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4941–4957, Online. Association for Computational Linguistics.