# Book Success Prediction with Pretrained Sentence Embeddings and Readability Scores

arXiv: https://arxiv.org/abs/2007.11073 · HTML: https://mlsanity.com/paper/2007.11073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Adaptive Data-Resilient Multi-Modal Hierarchical Multi-Label Book Genre Identification](https://mlsanity.com/paper/2505.03839/) (2025)

## Abstract (arXiv)

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.