  In this thesis, we investigated the relevance, faithfulness, and succinctness
aspects of Long Form Question Answering (LFQA). LFQA aims to generate an
in-depth, paragraph-length answer for a given question, to help bridge the gap
between real scenarios and the existing open-domain QA models which can only
extract short-span answers. LFQA is quite challenging and under-explored. Few
works have been done to build an effective LFQA system. It is even more
challenging to generate a good-quality long-form answer relevant to the query
and faithful to facts, since a considerable amount of redundant, complementary,
or contradictory information will be contained in the retrieved documents.
Moreover, no prior work has been investigated to generate succinct answers. We
are among the first to research the LFQA task. We pioneered the research
direction to improve the answer quality in terms of 1) query-relevance, 2)
answer faithfulness, and 3) answer succinctness.
