论文标题
事实增强的合成新闻
Fact-Enhanced Synthetic News Generation
论文作者
论文摘要
高级文本生成方法在文本摘要，语言翻译和综合新闻中取得了巨大的成功。但是，可以滥用这些技术来产生虚假信息和虚假新闻。为了更好地了解合成新闻的潜在威胁，我们开发了一种新一代方法，以生成高质量的新闻内容。现有的文本生成方法要么提供有限的补充信息，要么在输入和输出之间失去一致性，这使得合成新闻不值得信赖。为了解决这些问题，Factgen检索外部事实以丰富输出并从生成的内容中重建输入声明，以提高输入和输出之间的一致性。现实世界数据集的实验结果表明，Factgen的生成新闻内容是一致的，并且包含丰富的事实。如果使用FACTGEN来生成合成新闻，我们还讨论了可能识别这些合成新闻的可能辩护方法。
The advanced text generation methods have witnessed great success in text summarization, language translation, and synthetic news generation. However, these techniques can be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a new generation method FactGen to generate high-quality news content. The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy. To address these issues, FactGen retrieves external facts to enrich the output and reconstructs the input claim from the generated content to improve the consistency among the input and the output. Experiment results on real-world datasets show that the generated news contents of FactGen are consistent and contain rich facts. We also discuss the possible defending method to identify these synthetic news pieces if FactGen is used to generate synthetic news.
