  We propose a new approach to generate multiple variants of the target summary
with diverse content and varying lengths, then score and select admissible ones
according to users' needs. Abstractive summarizers trained on single reference
summaries may struggle to produce outputs that achieve multiple desirable
properties, i.e., capturing the most important information, being faithful to
the original, grammatical and fluent. In this paper, we propose a two-staged
strategy to generate a diverse set of candidate summaries from the source text
in stage one, then score and select admissible ones in stage two. Importantly,
our generator gives a precise control over the length of the summary, which is
especially well-suited when space is limited. Our selectors are designed to
predict the optimal summary length and put special emphasis on faithfulness to
the original text. Both stages can be effectively trained, optimized and
evaluated. Our experiments on benchmark summarization datasets suggest that
this paradigm can achieve state-of-the-art performance.
