  This paper tackles the goal of conclusion-supplement answer generation for
non-factoid questions, which is a critical issue in the field of Natural
Language Processing (NLP) and Artificial Intelligence (AI), as users often
require supplementary information before accepting a conclusion. The current
encoder-decoder framework, however, has difficulty generating such answers,
since it may become confused when it tries to learn several different long
answers to the same non-factoid question. Our solution, called an ensemble
network, goes beyond single short sentences and fuses logically connected
conclusion statements and supplementary statements. It extracts the context
from the conclusion decoder's output sequence and uses it to create
supplementary decoder states on the basis of an attention mechanism. It also
assesses the closeness of the question encoder's output sequence and the
separate outputs of the conclusion and supplement decoders as well as their
combination. As a result, it generates answers that match the questions and
have natural-sounding supplementary sequences in line with the context
expressed by the conclusion sequence. Evaluations conducted on datasets
including "Love Advice" and "Arts & Humanities" categories indicate that our
model outputs much more accurate results than the tested baseline models do.
