  We introduce 'extreme summarization', a new single-document summarization
task which aims at creating a short, one-sentence news summary answering the
question ``What is the article about?''. We argue that extreme summarization,
by nature, is not amenable to extractive strategies and requires an abstractive
modeling approach. In the hope of driving research on this task further: (a) we
collect a real-world, large scale dataset by harvesting online articles from
the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive
model which is conditioned on the article's topics and based entirely on
convolutional neural networks. We demonstrate experimentally that this
architecture captures long-range dependencies in a document and recognizes
pertinent content, outperforming an oracle extractive system and
state-of-the-art abstractive approaches when evaluated automatically and by
humans on the extreme summarization dataset.
