Personalized News Headline Generation System with Fine-grained User Modeling

Personalized news headline generation aims to sum-marize a news article as a news headline, according to the preference of a specific user. It can help users filter their interested news quickly and increase the news click rates for news providers. However, in this field, when learning user interests from their historically clicked news, existing research only learned user interests on word and news level, ignoring sentence level informativeness. This paper proposes a user model, adding sentence-level informativeness to learn user interests, and further guide the news headline generation. To be more detailed, based on attention layers, sentence and news are represented as the weighted sum of word and sentence representations, respectively. To further explore the correlation between different news contents (news title, body, and topic information), the query vectors in the attention layers are replaced by news content. Experiments on the dataset PENS show that the performance of these two models is better than the baseline model on the evaluation metrics ROUGE. Finally, some future directions are discussed, including interactions across informativeness levels and contents.