@inproceedings{xu-etal-2019-clickbait,
    title = "Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning",
    author = "Xu, Peng  and
      Wu, Chien-Sheng  and
      Madotto, Andrea  and
      Fung, Pascale",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D19-1303/",
    doi = "10.18653/v1/D19-1303",
    pages = "3065--3075",
    abstract = "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."
}