Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples

Krtin Kumar, Jackie Chi Kit Cheung


Abstract
Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets. We investigate how they achieve this performance with respect to human-written gold-standard abstracts, and whether the systems are able to understand deeper syntactic and semantic structures. We generate a set of contrastive summaries which are perturbed, deficient versions of human-written summaries, and test whether existing neural summarizers score them more highly than the human-written summaries. We analyze their performance on different datasets and find that these systems fail to understand the source text, in a majority of the cases.
Anthology ID:
N19-1396
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3949–3954
Language:
URL:
https://aclanthology.org/N19-1396
DOI:
10.18653/v1/N19-1396
Bibkey:
Cite (ACL):
Krtin Kumar and Jackie Chi Kit Cheung. 2019. Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3949–3954, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples (Kumar & Cheung, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1396.pdf