With the considerable success achieved by modern fuzzing infrastructures, more crashes are produced than ever before. To dig out the root cause, rapid and faithful crash triage for massive-sized crashes has always been attractive. However, hindered by the practical difficulty of reducing analysis imprecision without compromising efficiency, this goal has not been accomplished. \r\n\r\nIn this paper, we present an end-to-end crash triage solution DeFault, for accurately pinpointing unique root cause from massive crashes in a short time. In particular, we quantify the “crash relevance” of program entities based on mutual information, which serves as the criterion of unique crash bucketing and allows us to bucket massive crashes without pre-analyzing their root cause. The quantification of “crash relevance” is also used in the shortening of long crashing traces. On this basis, we use the interpretability of neural networks to precisely pinpoint the root cause in the shortened traces by evaluating each basic block’s impact on the crash label. Evaluated with 20 programs with 22216 crashes in total, DeFault demonstrates remarkable accuracy and performance, which is way beyond what the state-of-the-art techniques can achieve: crash de-duplication was achieved at a super-fast processing speed–0.017 second per crashing trace, without missing any unique bugs. After that, it identifies the root cause of 43 unique crashes with no false negatives and an average false positive rate of 9.2%.