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Missing sentence generation (or sentence infilling) fosters a wide range ofapplications in natural language generation, such as document auto-completionand meeting note expansion. This task asks the model to generate intermediatemissing sentences that can syntactically and semantically bridge thesurrounding context. Solving the sentence infilling task requires techniques innatural language processing ranging from understanding to discourse-levelplanning to generation. In this paper, we propose a framework to decouple thechallenge and address these three aspects respectively, leveraging the power ofexisting large-scale pre-trained models such as BERT and GPT-2. We empiricallydemonstrate the effectiveness of our model in learning a sentencerepresentation for generation and further generating a missing sentence thatfits the context.

INSET: Sentence Infilling with INter-SEntential Transformer