  Long-form question answering (LFQA) aims to generate a paragraph-length
answer for a given question. While current work on LFQA using large pre-trained
model for generation are effective at producing fluent and somewhat relevant
content, one primary challenge lies in how to generate a faithful answer that
has less hallucinated content. We propose a new end-to-end framework that
jointly models answer generation and machine reading. The key idea is to
augment the generation model with fine-grained, answer-related salient
information which can be viewed as an emphasis on faithful facts.
State-of-the-art results on two LFQA datasets, ELI5 and MS MARCO, demonstrate
the effectiveness of our method, in comparison with strong baselines on
automatic and human evaluation metrics. A detailed analysis further proves the
competency of our methods in generating fluent, relevant, and more faithful
answers.
