Before revision:   We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets.
After revision:  We propose Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths. The key enabler of our method is an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets.