论文标题
分析抽象文本摘要的多任务学习
Analyzing Multi-Task Learning for Abstractive Text Summarization
论文作者
论文摘要
尽管多任务学习和预先进行自然语言理解的成功取得了成功，但很少有作品研究任务家族对抽象文本摘要的影响。任务家庭是在结束前阶段学习共同技能（例如阅读理解）的一种任务分组的形式。为了缩小这一差距，我们使用任务系列对英语抽象文本摘要任务进行了多任务学习策略的影响。我们将任务分为三种策略之一，即顺序，同时和连续的多任务学习，并通过两个下游任务评估训练有素的模型。我们发现，任务家族的某些组合（例如，高级阅读理解和自然语言推论）对下游表现产生了积极影响。此外，我们发现任务家族的选择和组合比培训方案更能影响下游表现，从而支持使用任务家族进行抽象文本摘要。
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.
