Analyzing Sentence Fusion in Abstractive Summarization

Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu


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.
Anthology ID:
D19-5413
Volume:
Proceedings of the 2nd Workshop on New Frontiers in Summarization
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Lu Wang, Jackie Chi Kit Cheung, Giuseppe Carenini, Fei Liu
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
104–110
Language:
URL:
https://aclanthology.org/D19-5413
DOI:
10.18653/v1/D19-5413
Bibkey:
Cite (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.
Cite (Informal):
Analyzing Sentence Fusion in Abstractive Summarization (Lebanoff et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5413.pdf