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

Sotiris Lamprinidis, Daniel Hardt, Dirk Hovy

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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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