How Far are We from Robust Long Abstractive Summarization?

Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, Shirui Pan


Abstract
Abstractive summarization has made tremendous progress in recent years. In this work, we perform fine-grained human annotations to evaluate long document abstractive summarization systems (i.e., models and metrics) with the aim of implementing them to generate reliable summaries. For long document abstractive models, we show that the constant strive for state-of-the-art ROUGE results can lead us to generate more relevant summaries but not factual ones. For long document evaluation metrics, human evaluation results show that ROUGE remains the best at evaluating the relevancy of a summary. It also reveals important limitations of factuality metrics in detecting different types of factual errors and the reasons behind the effectiveness of BARTScore. We then suggest promising directions in the endeavor of developing factual consistency metrics. Finally, we release our annotated long document dataset with the hope that it can contribute to the development of metrics across a broader range of summarization settings.
Anthology ID:
2022.emnlp-main.172
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2682–2698
Language:
URL:
https://aclanthology.org/2022.emnlp-main.172
DOI:
10.18653/v1/2022.emnlp-main.172
Bibkey:
Cite (ACL):
Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, and Shirui Pan. 2022. How Far are We from Robust Long Abstractive Summarization?. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2682–2698, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
How Far are We from Robust Long Abstractive Summarization? (Koh et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-main.172.pdf
Dataset:
 2022.emnlp-main.172.dataset.zip