Abstract At present, most Chinese text summarization algorithms use the sequence-to-sequence model, but this model is prone to the problems of unknown words and incomplete content generation. To address these problems, we propose a new two-stage automatic text summarization method using keyword information and adversarial learning in this paper. On the one hand, the proposed method integrates the keyword information into the sequence-to-sequence model. The main information and keywords of the article are considered simultaneously through the attention mechanism to improve the information of summary generation. On the other hand, adversarial learning is introduced into the proposed model to avoid the problem that the semantic vector after passing through the encoder cannot save the context information better. Experiments are carried out on the Chinese dataset LCSTS, and the comparison results show that the proposed method has advantages in abstractive summarization.

中文翻译：

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一种使用关键字信息和对抗性学习的两阶段中文文本摘要算法

摘要 目前中文文本摘要算法大多采用序列到序列模型，但该模型容易出现生词不全和内容生成不完整的问题。为了解决这些问题，我们在本文中提出了一种使用关键字信息和对抗性学习的新的两阶段自动文本摘要方法。一方面，所提出的方法将关键字信息集成到序列到序列模型中。通过注意力机制同时考虑文章的主要信息和关键词，以提高摘要生成的信息。另一方面，在所提出的模型中引入了对抗性学习，以避免语义向量经过编码器后不能更好地保存上下文信息的问题。