# What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks

## TL;DR

This paper introduces a new extreme summarization task that generates concise, one-sentence news summaries using a novel CNN-based abstractive model conditioned on article topics, outperforming existing methods.

## Contribution

It presents a large-scale BBC dataset for extreme summarization and a novel CNN-based model that effectively captures long-range dependencies for abstractive summarization.

## Key findings

- The model outperforms extractive and state-of-the-art abstractive methods.
- It effectively captures long-range dependencies in documents.
- Human evaluations favor the proposed approach.

## Abstract

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.