Deep Differential Amplifier for Extractive Summarization

Ruipeng Jia, Yanan Cao, Fang Fang, Yuchen Zhou, Zheng Fang, Yanbing Liu, Shi Wang


Abstract
For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when maximizing the accuracy. The imbalanced classification of summarization is inherent, which can’t be addressed by common algorithms easily. In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework. Specifically, we first calculate and amplify the semantic difference between each sentence and all other sentences, and then apply the residual unit as the second item of the differential amplifier to deepen the architecture. Finally, to compensate for the imbalance, the corresponding objective loss of minority class is boosted by a weighted cross-entropy. In contrast to previous approaches, this model pays more attention to the pivotal information of one sentence, instead of all the informative context modeling by recurrent or Transformer architecture. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods. Our source code will be available on Github.
Anthology ID:
2021.acl-long.31
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
366–376
Language:
URL:
https://aclanthology.org/2021.acl-long.31
DOI:
10.18653/v1/2021.acl-long.31
Bibkey:
Cite (ACL):
Ruipeng Jia, Yanan Cao, Fang Fang, Yuchen Zhou, Zheng Fang, Yanbing Liu, and Shi Wang. 2021. Deep Differential Amplifier for Extractive Summarization. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 366–376, Online. Association for Computational Linguistics.
Cite (Informal):
Deep Differential Amplifier for Extractive Summarization (Jia et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.31.pdf
Video:
 https://aclanthology.org/2021.acl-long.31.mp4