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- W2890732265 abstract "Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns." @default.
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- W2890732265 date "2018-01-01" @default.
- W2890732265 modified "2023-10-05" @default.
- W2890732265 title "Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning" @default.
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- W2890732265 doi "https://doi.org/10.18653/v1/d18-1068" @default.
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