  We report on novel investigations into training models that make sentences
concise. We define the task and show that it is different from related tasks
such as summarization and simplification. For evaluation, we release two test
sets, consisting of 2000 sentences each, that were annotated by two and five
human annotators, respectively. We demonstrate that conciseness is a difficult
task for which zero-shot setups with large neural language models often do not
perform well. Given the limitations of these approaches, we propose a synthetic
data generation method based on round-trip translations. Using this data to
either train Transformers from scratch or fine-tune T5 models yields our
strongest baselines that can be further improved by fine-tuning on an
artificial conciseness dataset that we derived from multi-annotator machine
translation test sets.
