  While Transformer language models (LMs) are state-of-the-art for information
extraction, long text introduces computational challenges requiring suboptimal
preprocessing steps or alternative model architectures. Sparse attention LMs
can represent longer sequences, overcoming performance hurdles. However, it
remains unclear how to explain predictions from these models, as not all tokens
attend to each other in the self-attention layers, and long sequences pose
computational challenges for explainability algorithms when runtime depends on
document length. These challenges are severe in the medical context where
documents can be very long, and machine learning (ML) models must be auditable
and trustworthy. We introduce a novel Masked Sampling Procedure (MSP) to
identify the text blocks that contribute to a prediction, apply MSP in the
context of predicting diagnoses from medical text, and validate our approach
with a blind review by two clinicians. Our method identifies about 1.7x more
clinically informative text blocks than the previous state-of-the-art, runs up
to 100x faster, and is tractable for generating important phrase pairs. MSP is
particularly well-suited to long LMs but can be applied to any text classifier.
We provide a general implementation of MSP.
