@inproceedings{lebanoff-etal-2019-analyzing,
title = "Analyzing Sentence Fusion in Abstractive Summarization",
author = "Lebanoff, Logan and
Muchovej, John and
Dernoncourt, Franck and
Kim, Doo Soon and
Kim, Seokhwan and
Chang, Walter and
Liu, Fei",
editor = "Wang, Lu and
Cheung, Jackie Chi Kit and
Carenini, Giuseppe and
Liu, Fei",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5413",
doi = "10.18653/v1/D19-5413",
pages = "104--110",
abstract = "While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences. In this paper, we analyze the outputs of five state-of-the-art abstractive summarizers, focusing on summary sentences that are formed by sentence fusion. We ask assessors to judge the grammaticality, faithfulness, and method of fusion for summary sentences. Our analysis reveals that system sentences are mostly grammatical, but often fail to remain faithful to the original article.",
}
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<abstract>While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences. In this paper, we analyze the outputs of five state-of-the-art abstractive summarizers, focusing on summary sentences that are formed by sentence fusion. We ask assessors to judge the grammaticality, faithfulness, and method of fusion for summary sentences. Our analysis reveals that system sentences are mostly grammatical, but often fail to remain faithful to the original article.</abstract>
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%0 Conference Proceedings
%T Analyzing Sentence Fusion in Abstractive Summarization
%A Lebanoff, Logan
%A Muchovej, John
%A Dernoncourt, Franck
%A Kim, Doo Soon
%A Kim, Seokhwan
%A Chang, Walter
%A Liu, Fei
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the 2nd Workshop on New Frontiers in Summarization
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lebanoff-etal-2019-analyzing
%X While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences. In this paper, we analyze the outputs of five state-of-the-art abstractive summarizers, focusing on summary sentences that are formed by sentence fusion. We ask assessors to judge the grammaticality, faithfulness, and method of fusion for summary sentences. Our analysis reveals that system sentences are mostly grammatical, but often fail to remain faithful to the original article.
%R 10.18653/v1/D19-5413
%U https://aclanthology.org/D19-5413
%U https://doi.org/10.18653/v1/D19-5413
%P 104-110
Markdown (Informal)
[Analyzing Sentence Fusion in Abstractive Summarization](https://aclanthology.org/D19-5413) (Lebanoff et al., 2019)
ACL
- Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, and Fei Liu. 2019. Analyzing Sentence Fusion in Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 104–110, Hong Kong, China. Association for Computational Linguistics.