Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization

Haoran Li, Junnan Zhu, Jiajun Zhang, Chengqing Zong


Abstract
In this paper, we investigate the sentence summarization task that produces a summary from a source sentence. Neural sequence-to-sequence models have gained considerable success for this task, while most existing approaches only focus on improving the informativeness of the summary, which ignore the correctness, i.e., the summary should not contain unrelated information with respect to the source sentence. We argue that correctness is an essential requirement for summarization systems. Considering a correct summary is semantically entailed by the source sentence, we incorporate entailment knowledge into abstractive summarization models. We propose an entailment-aware encoder under multi-task framework (i.e., summarization generation and entailment recognition) and an entailment-aware decoder by entailment Reward Augmented Maximum Likelihood (RAML) training. Experiment results demonstrate that our models significantly outperform baselines from the aspects of informativeness and correctness.
Anthology ID:
C18-1121
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1430–1441
Language:
URL:
https://www.aclweb.org/anthology/C18-1121
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-1121.pdf