Sensational headlines are headlines that capture people's attention and generate reader interest. Conventional abstractive headline generation methods, unlike human writers, do not optimize for maximal reader attention. In this paper, we propose a model that generates sensational headlines without labeled data. We first train a sensationalism scorer by classifying online headlines with many comments ("clickbait") against a baseline of headlines generated from a summarization model. The score from the sensationalism scorer is used as the reward for a reinforcement learner. However, maximizing the noisy sensationalism reward will generate unnatural phrases instead of sensational headlines. To effectively leverage this noisy reward, we propose a novel loss function, Auto-tuned Reinforcement Learning (ARL), to dynamically balance reinforcement learning (RL) with maximum likelihood estimation (MLE). Human evaluation shows that 60.8% of samples generated by our model are sensational, which is significantly better than the Pointer-Gen baseline and other RL models.
LLM-guided headline rewriting for clickability enhancement without clickbait
Intelligent Systems, Afeka Academic College of Engineering（阿菲卡工程学院智能系统系）；School of Computer Science, Faculty of Sciences, HIT-Holon Institute of Technology（霍隆理工学院理学院计算机科学系）
Prompt-tuning for Clickbait Detection via Text Summarization
School of Information Engineering, Yangzhou University（扬州大学信息工程学院）；School of Electronics and Information Engineering, Anhui Jianzhu University（安徽建筑大学电子与信息工程学院）
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
Indian Institute of Technology, Kharagpur（印度理工学院克勒格布尔分校）；University of Maryland, College Park（马里兰大学帕克分校）；University of North Carolina, Charlotte（北卡罗来纳大学夏洛特分校）
ClickGuard: A Trustworthy Adaptive Fusion Framework for Clickbait Detection
Department of Computer Science & Engineering, Bhagwan Parshuram Institute of Technology, Delhi, India（印度德里巴关·帕舒拉姆理工学院计算机科学与工程系）；Department of Electronics & Communication Engineering, Bhagwan Parshuram Institute of Technology, Delhi, India（印度德里巴关·帕舒拉姆理工学院电子与通信工程系）；Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India（印度德里德里理工大学电子与通信工程系）
