  Predicting the potential success of a book in advance is vital in many
applications. This could help both publishers and readers in their
decision-making process whether or not a book is worth publishing and reading,
respectively. In this paper, we propose a model that leverages pretrained
sentence embeddings along with various readability scores for book success
prediction. Unlike previous methods, the proposed method requires no
count-based, lexical, or syntactic features. Instead, we use a convolutional
neural network over pretrained sentence embeddings and leverage different
readability scores through a simple concatenation operation. Our proposed model
outperforms strong baselines for this task by as large as 6.4\% F1-score
points. Moreover, our experiments show that according to our model, only the
first 1K sentences are good enough to predict the potential success of books.
