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.

本研究提出了一种名为 Masque 的多风格抽象化摘要模型，该模型关注于从问题和多个文本段落中生成摘要，以涵盖现实世界应用所需的各种答案风格，并通过将多风格答案学习到一个模型中来提高自然语言生成能力，实验表明该模型在多个任务中的表现都达到了最新的最好水平。