What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain

Annie Louis, Ani Nenkova


Abstract
Great writing is rare and highly admired. Readers seek out articles that are beautifully written, informative and entertaining. Yet information-access technologies lack capabilities for predicting article quality at this level. In this paper we present first experiments on article quality prediction in the science journalism domain. We introduce a corpus of great pieces of science journalism, along with typical articles from the genre. We implement features to capture aspects of great writing, including surprising, visual and emotional content, as well as general features related to discourse organization and sentence structure. We show that the distinction between great and typical articles can be detected fairly accurately, and that the entire spectrum of our features contribute to the distinction.
Anthology ID:
Q13-1028
Volume:
Transactions of the Association for Computational Linguistics, Volume 1
Month:
Year:
2013
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
341–352
Language:
URL:
https://www.aclweb.org/anthology/Q13-1028
DOI:
10.1162/tacl_a_00232
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PDF:
http://aclanthology.lst.uni-saarland.de/Q13-1028.pdf