Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

Understand

The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.

• A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers.
• However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition.
• In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization.