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.
REF defined genre-specific and general features to predict the article quality in science journalism domain.
What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science