Analyzing Multi-Task Learning for Abstractive Text Summarization
This work addresses the impact of task families on abstractive text summarization, offering incremental insights for NLP researchers.
The study analyzed how grouping tasks into families during multi-task learning affects abstractive text summarization, finding that specific combinations like advanced reading comprehension and natural language inference improve downstream performance more than the training scheme.
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.