Before revision:   We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets.
After revision:  At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical. We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets.