@inproceedings{honda-etal-2018-pruning,
title = "Pruning Basic Elements for Better Automatic Evaluation of Summaries",
author = "Honda, Ukyo and
Hirao, Tsutomu and
Nagata, Masaaki",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2104",
doi = "10.18653/v1/N18-2104",
pages = "661--666",
abstract = "We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE). Although the BE concept is widely used for the automated evaluation of summaries, its weakness is that it redundantly matches basic elements. To avoid this redundancy, pBE prunes basic elements by (1) disregarding frequency count of basic elements and (2) reducing semantically overlapped basic elements based on word similarity. Even though it is simple, pBE outperforms ROUGE in DUC datasets in most cases and achieves the highest rank correlation coefficient in TAC 2011 AESOP task.",
}
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%0 Conference Proceedings
%T Pruning Basic Elements for Better Automatic Evaluation of Summaries
%A Honda, Ukyo
%A Hirao, Tsutomu
%A Nagata, Masaaki
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F honda-etal-2018-pruning
%X We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE). Although the BE concept is widely used for the automated evaluation of summaries, its weakness is that it redundantly matches basic elements. To avoid this redundancy, pBE prunes basic elements by (1) disregarding frequency count of basic elements and (2) reducing semantically overlapped basic elements based on word similarity. Even though it is simple, pBE outperforms ROUGE in DUC datasets in most cases and achieves the highest rank correlation coefficient in TAC 2011 AESOP task.
%R 10.18653/v1/N18-2104
%U https://aclanthology.org/N18-2104
%U https://doi.org/10.18653/v1/N18-2104
%P 661-666
Markdown (Informal)
[Pruning Basic Elements for Better Automatic Evaluation of Summaries](https://aclanthology.org/N18-2104) (Honda et al., NAACL 2018)
ACL
- Ukyo Honda, Tsutomu Hirao, and Masaaki Nagata. 2018. Pruning Basic Elements for Better Automatic Evaluation of Summaries. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 661–666, New Orleans, Louisiana. Association for Computational Linguistics.