Abstract- Abduction is a from logical inference that seeks out best explanations for a given observation. Abduction has al ready been well studied in the field of computational logic, and logic programming in particular. In contextual abduction, explanations obtained within one context may also be relevant in different contexts. In such contextual abduction, explanations thus can be reused with little cost. When abduction is realized in logic programming, one can reuse previously obtained explanations from one contexts to another by benefiting from a logic programming feature called tabling. In this paper, we revisit tabling in contextual abduction and improve this technique subsumption. The employment of answer subsumption in this minimal explanations for an observation. It also helps avoid tabling to many and large explanations for a given observation, which may fail contextual abduction in practice as it requires too many resources before being able to return a solution. We provide a prototype, TABDUAL, of this improved technique as a proof of concept. Our experiments, booth in artificial and real words cases, show that's TABDUAL correctly returns minimal explanations, while the cost of their computation is greatly reduced.