Our analysis exhibits that our model performs comparably to state-of-the-artwork approaches on domains which are comparable, while performing significantly higher on highly divergent domains. Our evaluations point out that the proposed approach tunes summaries to the goal vocabulary whereas still maintaining a superior abstract quality against a state-of-the-art word embedding primarily based lexical substitution algorithm, suggesting the feasibility of the proposed strategy.

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