Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning
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.- Anthology ID:
- D18-1068
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 659–664
- Language:
- URL:
- https://aclanthology.org/D18-1068
- DOI:
- 10.18653/v1/D18-1068
- Cite (ACL):
- Sotiris Lamprinidis, Daniel Hardt, and Dirk Hovy. 2018. Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 659–664, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning (Lamprinidis et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D18-1068.pdf