CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems

Yiran Chen, Pengfei Liu, Ming Zhong, Zi-Yi Dou, Danqing Wang, Xipeng Qiu, Xuanjing Huang


Abstract
Neural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization. However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained and evaluated on the same dataset. We argue that this approach can narrow our understanding of the generalization ability for different summarization systems. In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora. A comprehensive study of 11 representative summarization systems on 5 datasets from different domains reveals the effect of model architectures and generation ways (i.e. abstractive and extractive) on model generalization ability. Further, experimental results shed light on the limitations of existing summarizers. Brief introduction and supplementary code can be found in https://github.com/zide05/CDEvalSumm.
Anthology ID:
2020.findings-emnlp.329
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3679–3691
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.329
DOI:
10.18653/v1/2020.findings-emnlp.329
Bibkey:
Cite (ACL):
Yiran Chen, Pengfei Liu, Ming Zhong, Zi-Yi Dou, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2020. CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3679–3691, Online. Association for Computational Linguistics.
Cite (Informal):
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems (Chen et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.329.pdf
Optional supplementary material:
 2020.findings-emnlp.329.OptionalSupplementaryMaterial.zip
Code
 atulkum/pointer_summarizer +  additional community code
Data
BigPatentCNN/Daily MailReddit TIFU