Generating Topic-Preserving Synthetic News

Ahmadreza Mosallanezhad; Kai Shu; Huan Liu

doi:10.1109/BigData52589.2021.9671623

Generating Topic-Preserving Synthetic News

Ahmadreza Mosallanezhad, Kai Shu, Huan Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
Editors	Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	490-499
Number of pages	10
ISBN (Electronic)	9781665439022
DOIs	https://doi.org/10.1109/BigData52589.2021.9671623
State	Published - 2021
Event	2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference	2021 IEEE International Conference on Big Data, Big Data 2021
Country/Territory	United States
City	Virtual, Online
Period	12/15/21 → 12/18/21

Keywords

Adversarial Training
Reinforcement Learning
Text Generation

ASJC Scopus subject areas

Information Systems and Management
Artificial Intelligence
Computer Vision and Pattern Recognition
Information Systems

Access to Document

10.1109/BigData52589.2021.9671623

Cite this

Mosallanezhad, A., Shu, K., & Liu, H. (2021). Generating Topic-Preserving Synthetic News. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. T. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 (pp. 490-499). (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData52589.2021.9671623

Generating Topic-Preserving Synthetic News. / Mosallanezhad, Ahmadreza; Shu, Kai; Liu, Huan.
Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. ed. / Yixin Chen; Heiko Ludwig; Yicheng Tu; Usama Fayyad; Xingquan Zhu; Xiaohua Tony Hu; Suren Byna; Xiong Liu; Jianping Zhang; Shirui Pan; Vagelis Papalexakis; Jianwu Wang; Alfredo Cuzzocrea; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. p. 490-499 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mosallanezhad, A, Shu, K & Liu, H 2021, Generating Topic-Preserving Synthetic News. in Y Chen, H Ludwig, Y Tu, U Fayyad, X Zhu, XT Hu, S Byna, X Liu, J Zhang, S Pan, V Papalexakis, J Wang, A Cuzzocrea & C Ordonez (eds), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, Institute of Electrical and Electronics Engineers Inc., pp. 490-499, 2021 IEEE International Conference on Big Data, Big Data 2021, Virtual, Online, United States, 12/15/21. https://doi.org/10.1109/BigData52589.2021.9671623

Mosallanezhad A, Shu K, Liu H. Generating Topic-Preserving Synthetic News. In Chen Y, Ludwig H, Tu Y, Fayyad U, Zhu X, Hu XT, Byna S, Liu X, Zhang J, Pan S, Papalexakis V, Wang J, Cuzzocrea A, Ordonez C, editors, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 490-499. (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). doi: 10.1109/BigData52589.2021.9671623

Mosallanezhad, Ahmadreza ; Shu, Kai ; Liu, Huan. / Generating Topic-Preserving Synthetic News. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. editor / Yixin Chen ; Heiko Ludwig ; Yicheng Tu ; Usama Fayyad ; Xingquan Zhu ; Xiaohua Tony Hu ; Suren Byna ; Xiong Liu ; Jianping Zhang ; Shirui Pan ; Vagelis Papalexakis ; Jianwu Wang ; Alfredo Cuzzocrea ; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 490-499 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

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title = "Generating Topic-Preserving Synthetic News",

abstract = "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.",

keywords = "Adversarial Training, Reinforcement Learning, Text Generation",

author = "Ahmadreza Mosallanezhad and Kai Shu and Huan Liu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",

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AU - Shu, Kai

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PY - 2021

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N2 - 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.

AB - 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.

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A2 - Chen, Yixin

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A2 - Zhu, Xingquan

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Generating Topic-Preserving Synthetic News

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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