StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation

Stylistic headline generation is the task to generate a headline that not only summarizes the content of an article, but also reflects a desired style that attracts users. As style-specific article-headline pairs are scarce, previous researches focus on unsupervised approaches with a standard headline generation dataset and mono-style corpora. In this work, we follow this line and propose StyleBART, an unsupervised approach for stylistic headline generation. Our method decorates the pretrained BART model with adapters that are responsible for different styles and allows the generation of headlines with diverse styles by simply switching the adapters. Different from previous works, StyleBART separates the task of style learning and headline generation, making it possible to freely combine the base model and the style adapters during inference. We further propose an inverse paraphrasing task to enhance the style adapters. Extensive automatic and human evaluations show that StyleBART achieves new state-of-the-art performance in the unsupervised stylistic headline generation task, producing high-quality headlines with the desired style.Comment: Findings of EMNLP 202

What's in a name? the UK newspapers' fabrication and commodification of Foxy Knoxy

This chapter analyses how, immediately after the arrest of Amanda Knox, the UK’s national press played a pivotal role in transforming the American student into ‘Foxy Knoxy’, the duplicitous, psychologically disturbed femme fatale who orchestrated and participated in the sexually motivated murder of her flatmate, Meredith Kercher. This case exemplifies what happens when UK reporting restrictions do not apply, leaving journalists free to employ imaginative practices to create the infotainment spectacle that ‘Foxy Knoxy’ became and to ignore her legal right to a presumption of innocence. It is also the first example of journalists mining suspects’ social media sites and re-contextualising their text and images to provide ‘evidential’ sources of a guilty persona

MEDIA DISCOURSE AND INTERNAL CONFLICT OF JAVANESE POWER IN YOGYAKARTA

This paper attempts to reveal how the local media, Kedaulatan Rakyat, produces discourse on the Yogyakarta palace’s internal conflict involving Sri Sultan Hamengku Buwono X and his siblings. The Special Region of Yogyakarta as a province in Indonesia has a special government system that is different from other regions. The governor of Yogyakarta is not elected by the people like in other regions in Indonesia but is determined by the DPRD based on who serves as the King of the Yogyakarta Palace. This phenomenon becomes interesting when there are differences of opinion among the people and within the Yogyakarta Palace regarding the acceptance of women leaders. The conflict began to arise when Sultan Hamengku Buwono X issued the Sabda Tama and Sabda Raja (Sabda Raja) which were interpreted as the Sultan’s way to pave the way for his daughter to become heir to the throne. The Sultan’s younger siblings opposed the female king to rule in Yogyakarta, so an internal conflict arose. The research method used is a qualitative method with a critical discourse analysis approach using the Norman Fairclough model to analyze news texts related to the Yogyakarta Palace’s internal conflict from the Kedaulatan Rakyat Daily. From the results of the research, it was found that the Kedaulatan Rakyat Daily produced discourses on internal conflict in the Yogyakarta palace by representing the ideology and interests of Sultan HB X’s younger siblings. This local daily newspaper also commodified internal conflict in the Yogyakarta palace as events that were produced and disseminated to the public for the benefits media economy

Critical Discourse Analysis of Tabloid Headlines

This paper analyses headlines appearing on tabloid front pages (the Croatian 24 sata and the British Daily Star) in order to determine how they capture the attention of their potential readers. A corpus of tabloid front pages was analysed using methods laid out by Critical Discourse Analysis and a focus group session was conducted in order to complement the results of the analysis. It was found out that tabloids offer to their readers a selective view of the world in which the presentation of certain events is overly exaggerated and the events are offered to the readers in a linguistically complex way worthy of further research. Specifically, this is achieved by making certain events sound shocking and unexpected, thus destabilizing the readers' view of the world and capturing their attention. On the other hand, celebrities are presented in an intimate and revealing way in order to bring them closer to the readers. Both of these strategies have been found in both Croatian and English tabloids. The focus group was mostly aware of them

J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News

The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape. Among various types of AI-generated text, AI-generated news presents a significant threat as it can be a prominent source of misinformation online. While several recent efforts have focused on detecting AI-generated text in general, these methods require enhanced reliability, given concerns about their vulnerability to simple adversarial attacks. Furthermore, due to the eccentricities of news writing, applying these detection methods for AI-generated news can produce false positives, potentially damaging the reputation of news organizations. To address these challenges, we leverage the expertise of an interdisciplinary team to develop a framework, J-Guard, capable of steering existing supervised AI text detectors for detecting AI-generated news while boosting adversarial robustness. By incorporating stylistic cues inspired by the unique journalistic attributes, J-Guard effectively distinguishes between real-world journalism and AI-generated news articles. Our experiments on news articles generated by a vast array of AI models, including ChatGPT (GPT3.5), demonstrate the effectiveness of J-Guard in enhancing detection capabilities while maintaining an average performance decrease of as low as 7% when faced with adversarial attacks.Comment: This Paper is Accepted to The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL 2023