Abstract
Enhancing reader engagement while preserving informational fidelity is a central challenge in controllable text generation for news media. Optimizing news headlines for reader engagement is often conflated with clickbait, resulting in exaggerated or misleading phrasing that undermines editorial trust. We frame clickbait not as a separate stylistic category, but as an extreme outcome of disproportionate amplification of otherwise legitimate engagement cues. Based on this view, we formulate headline rewriting as a controllable generation problem, where specific engagement-oriented linguistic attributes are selectively strengthened under explicit constraints on semantic faithfulness and proportional emphasis. We present a guided headline rewriting framework built on a large language model (LLM) that uses the Future Discriminators for Generation (FUDGE) paradigm for inference-time control. The LLM is steered by two auxiliary guide models: (1) a clickbait scoring model that provides negative guidance to suppress excessive stylistic amplification, and (2) an engagement-attribute model that provides positive guidance aligned with target clickability objectives. Both guides are trained on neutral headlines drawn from a curated real-world news corpus. At the same time, clickbait variants are generated synthetically by rewriting these original headlines using an LLM under controlled activation of predefined engagement tactics. By adjusting guidance weights at inference time, the system generates headlines along a continuum from neutral paraphrases to more engaging yet editorially acceptable formulations. The proposed framework provides a principled approach for studying the trade-off between attractiveness, semantic preservation, and clickbait avoidance, and supports responsible LLM-based headline optimization in journalistic settings.
Abstract (translated)
在保持信息保真度的同时提升读者参与度，是新闻媒体可控文本生成的核心挑战。优化新闻标题以吸引读者常与点击诱饵混为一谈，导致夸张或误导性表述，从而损害编辑公信力。本研究将点击诱饵视为一种独立文体类别，而是 legitimate engagement cues 被不成比例放大的极端结果。基于此视角，我们将标题重写构建为可控生成问题：在语义忠实度与比例性强调的明确约束下，选择性地强化特定面向参与度的语言特征。我们提出一种基于大语言模型（LLM）的引导式标题重写框架，采用 Future Discriminators for Generation（FUDGE）范式实现推理时控制。该LLM由两个辅助引导模型驱动：（1）点击诱饵评分模型提供负向引导，抑制过度的文体放大；（2）参与度属性模型提供与目标点击率对齐的正向引导。两模型均基于精选的真实新闻语料库中的中性标题训练。同时，通过LLM在受控激活预定义参与策略条件下重写原始标题，合成生成点击诱饵变体。通过调整推理时的引导权重，系统可在中性释义与更具吸引力且编辑可接受的表述之间生成连续谱系的标题。该框架为研究吸引力、语义保持与点击诱饵规避之间的权衡提供了原则性方法，并支持新闻场景中基于LLM的负责任标题优化。
URL
https://arxiv.org/abs/2603.22459
