Abstract
This study tackles generative reading comprehension (RC), which consists of answering
questions based on textual evidence and natural language generation (NLG). We propose
a multi-style abstractive summarization model
for question answering, called Masque. The
proposed model has two key characteristics.
First, unlike most studies on RC that have focused on extracting an answer span from the
provided passages, our model instead focuses
on generating a summary from the question
and multiple passages. This serves to cover
various answer styles required for real-world
applications. Second, whereas previous studies built a specific model for each answer style
because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved. This also enables our model to give an answer in the target style. Experiments show that our model
achieves state-of-the-art performance on the
Q&A task and the Q&A + NLG task of MS
MARCO 2.1 and the summary task of NarrativeQA. We observe that the transfer of the
style-independent NLG capability to the target
style is the key to its success.