Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning

Sotiris Lamprinidis, Daniel Hardt, Dirk Hovy


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
Bibkey:
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)
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PDF:
https://aclanthology.org/D18-1068.pdf
Video:
 https://aclanthology.org/D18-1068.mp4