Generative Adversarial Network for Abstractive Text Summarization
文献类型：会议论文
| 作者 | Linqing Liu; Yao Lu; Min Yang; Qiang Qu; Jia Zhu |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | New Orleans, Louisiana, USA |
| 英文摘要 | In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries1. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14096]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Linqing Liu,Yao Lu,Min Yang,et al. Generative Adversarial Network for Abstractive Text Summarization[C]. 见:. New Orleans, Louisiana, USA. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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