Generative Adversarial Network for Joint Headline and Summary Generation

• Published in: IEEE access 2022, Vol.10, p.90745-90751
• Main Authors: Lin, Ching-Sheng, Jwo, Jung-Sing, Lee, Cheng-Hsiung, Hsieh, Tsai-Feng
• Format: Article
• Language: English
• Subjects: Annotations; Decoding; Electronic documents; generative adversarial network; Generative adversarial networks; Generators; Headline; joint model; Machine learning; Newsrooms; Representations; summary; Task analysis; Training; Training data; Transformers; Unsupervised learning; unsupervised training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3202205

With the ever-increasing amount of electronic documents being generated, it is imperative to provide an intuitive headline and a concise summary of the document to help readers quickly get the gist without going through the all details. While humans have strong abilities to create headlines and generate summaries, the automatic text generation of this research field is still challenging due to the difficult language understanding and complex text synthesis. Moreover, human annotation for machine learning is another matter needed to be addressed. In this paper, we propose a joint model to resolve aforementioned issues simultaneously. To perform unsupervised training to reduce labor cost, we employ a generative adversarial network (GAN) without parallel training data. Considering that the headline and summary have strong relationship, we propose a joint model to learn better representations by including an additional type representation into the GAN. Experiments are conducted on the public dataset, NEWS-ROOM. The experimental results demonstrate that our approach is able to effectively create a reasonable headline as well as a concise summary.