Generating Topic-Preserving Synthetic News
Generating Topic-Preserving Synthetic News
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DOI：
10.1109/bigdata52589.2021.9671623
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Ahmadreza Mosallanezhad;Kai Shu;Huan Liu
中科院分区：
文献类型：
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作者：
Ahmadreza Mosallanezhad;Kai Shu;Huan Liu
文本生成方法在文本摘要、机器翻译和合成新闻生成等方面取得了巨大的成功。然而，这些技术可能会被滥用，产生虚假信息和假新闻。为了更好地理解合成新闻的潜在威胁，我们开发了一种新的生成方法RLTG来生成主题保持新闻内容。现有的大多数文本生成方法要么受特定属性的控制，要么在输入声明和输出新闻之间缺乏主题一致性，使得合成新闻不太连贯和真实。在本文中，我们通过提出一种新的基于深度强化学习的方法来控制大型预训练语言模型的输出，来研究主题保持合成新闻生成的问题。在真实数据集上的实验结果表明，RLTG生成的新闻内容具有主题一致性和真实感。
The text generation methods have witnessed great success in text summarization, machine translation, and synthetic news generation. However, these techniques may be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a novel generation method RLTG to generate topic-preserving news content. The majority of existing text generation methods are either controlled by specific attributes or lack topic consistency between the input claims and output news, making synthetic news less coherent and realistic. In this paper, we study the problem of topic-preserving synthetic news generation by proposing a novel deep reinforcement learning-based method to control the output of large pre-trained language models. Experiment results on real-world datasets demonstrate that the news contents generated by RLTG are topic-consistent and realistic.