Paper: Automatic Prediction of Aesthetics and Interestingness of Text Passages

ACL ID C14-1086
Title Automatic Prediction of Aesthetics and Interestingness of Text Passages
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

This paper investigates the problem of automated text aesthetics prediction. The avail- ability of user generated content and ratings, e.g. Flickr, has induced research in aesthet- ics prediction for non-text domains, particularly for photographic images. This problem, however, has yet not been explored for the text domain. Due to the very subjective nature of text aesthetics, it is difficult to compile human annotated data by methods such as crowd sourcing with a fair degree of inter-annotator agreement. The availability of the Kindle ?popular highlights? data has motivated us to compile a dataset com- prised of human annotated aesthetically pleasing and interesting text passages. We then undertake a supervised classification approach to predict text aesthetics by constructing real-valued...