Before revision:   We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets. MLS employs a pair of supervised sequence-to-sequence networks. The first network called theMFS-Net constructs a minimal feasible summary by identifying the key concepts of the input document .
After revision:  We propose MLS, an MFS-Net constructs a minimal feasible summary by identifying the key concepts of the input document .