CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
Li, Haoran; Zhu, Junnan; Zhang, Jiajun; Zong, Chengqing
2018
Conference NameCOLING
Conference Date2018-8
Conference PlaceUSA
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 word overlap between the generated summary and the reference, which ignore the correctness, i.e., the summary should not contain error messages 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. Experimental results demonstrate that our models significantly outperform baselines from the aspects of informativeness and correctness.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23206
Collection模式识别国家重点实验室_自然语言处理
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Haoran,Zhu, Junnan,Zhang, Jiajun,et al. Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization[C],2018.
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