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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/D19-5413.pdf