Automatizing biocurators' intuition: filtering scientific papers by analyzing titles and short summaries

We present a text classification task arising in the biocuration of cellular chemical reactions when searching for curatable literature. We explore the suitability of various NLP and ML methods for this task. In summary, while fine-tuned domain-specific language models show the best results, random forests are nearly as good, with a much lighter computational footprint.