@inproceedings{cai-etal-2023-generating,
    title = "Generating User-Engaging News Headlines",
    author = "Cai, Pengshan  and
      Song, Kaiqiang  and
      Cho, Sangwoo  and
      Wang, Hongwei  and
      Wang, Xiaoyang  and
      Yu, Hong  and
      Liu, Fei  and
      Yu, Dong",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.183",
    doi = "10.18653/v1/2023.acl-long.183",
    pages = "3265--3280",
    abstract = "The potential choices for news article headlines are enormous, and finding the right balance between conveying the essential message and capturing the reader{'}s attention is key to effective headlining. However, presenting the same news headline to all readers is a suboptimal strategy, because it does not take into account the different preferences and interests of diverse readers, who may be confused about why a particular article has been recommended to them and do not see a clear connection between their interests and the recommended article. In this paper, we present a novel framework that addresses these challenges by incorporating user profiling to generate personalized headlines, and a combination of automated and human evaluation methods to determine user preference for personalized headlines. Our framework utilizes a learnable relevance function to assign personalized signature phrases to users based on their reading histories, which are then used to personalize headline generation. Through extensive evaluation, we demonstrate the effectiveness of our proposed framework in generating personalized headlines that meet the needs of a diverse audience. Our framework has the potential to improve the efficacy of news recommendations and facilitate creation of personalized content.",
}

The potential choices for news article headlines are enormous, and finding the right balance between conveying the essential message and capturing the reader{'}s attention is key to effective headlining. However, presenting the same news headline to all readers is a suboptimal strategy, because it does not take into account the different preferences and interests of diverse readers, who may be confused about why a particular article has been recommended to them and do not see a clear connection between their interests and the recommended article. In this paper, we present a novel framework that addresses these challenges by incorporating user profiling to generate personalized headlines, and a combination of automated and human evaluation methods to determine user preference for personalized headlines. Our framework utilizes a learnable relevance function to assign personalized signature phrases to users based on their reading histories, which are then used to personalize headline generation. Through extensive evaluation, we demonstrate the effectiveness of our proposed framework in generating personalized headlines that meet the needs of a diverse audience. Our framework has the potential to improve the efficacy of news recommendations and facilitate creation of personalized content.
Cai, Pengshan Song, Kaiqiang Cho, Sangwoo Wang, Hongwei Wang, Xiaoyang Yu, Hong Liu, Fei Yu, Dong
Generating User-Engaging News Headlines