Abstract: Highlights•We propose a novel summarization based defence, D-NEXUS, against attacks on the sentiment analysis models.•We are the first to study the applicability of summarization for defending the sentiment analysis models.•Unlike the existing spelling correction based defenses, D-NEXUS successfully mitigates the state-of-the-art attacks involving word replacement, insertion, and deletion strategies.•Extensive experiments on publicly available datasets show that D-NEXUS successfully defends against state-of-the-art attacks.•D-NEXUS is model-agnostic and can provide defense in a time-efficient manner.
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