  Automatic argument generation is an appealing but challenging task. In this
paper, we study the specific problem of counter-argument generation, and
present a novel framework, CANDELA. It consists of a powerful retrieval system
and a novel two-step generation model, where a text planning decoder first
decides on the main talking points and a proper language style for each
sentence, then a content realization decoder reflects the decisions and
constructs an informative paragraph-level argument. Furthermore, our generation
model is empowered by a retrieval system indexed with 12 million articles
collected from Wikipedia and popular English news media, which provides access
to high-quality content with diversity. Automatic evaluation on a large-scale
dataset collected from Reddit shows that our model yields significantly higher
BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial
comparisons. Human evaluation further indicates that our system arguments are
more appropriate for refutation and richer in content.
