  Despite the recent success of multi-task learning and pre-finetuning for
natural language understanding, few works have studied the effects of task
families on abstractive text summarization. Task families are a form of task
grouping during the pre-finetuning stage to learn common skills, such as
reading comprehension. To close this gap, we analyze the influence of
multi-task learning strategies using task families for the English abstractive
text summarization task. We group tasks into one of three strategies, i.e.,
sequential, simultaneous, and continual multi-task learning, and evaluate
trained models through two downstream tasks. We find that certain combinations
of task families (e.g., advanced reading comprehension and natural language
inference) positively impact downstream performance. Further, we find that
choice and combinations of task families influence downstream performance more
than the training scheme, supporting the use of task families for abstractive
text summarization.
