Finding decision jumps in text classification.

•We propose Jumper, a novel framework that models text classification as a sequential decision process.•Experiments show that Jumper makes decisions whenever the evidence is enough, therefore reducing total text reading by 30-40% and often finding the key rationale of prediction.•Jumper achieves classification accuracy better than or comparable to state-of-the-art models in several benchmarks and industrial datasets.•Jumper is able to make a decision at the theoretically optimal decision position.