Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Shashi Narayan, Shay B. Cohen, Mirella Lapata


Abstract
We introduce “extreme summarization”, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question “What is the article about?”. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We 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.
Anthology ID:
D18-1206
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1797–1807
Language:
URL:
https://aclanthology.org/D18-1206
DOI:
10.18653/v1/D18-1206
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/D18-1206.pdf
Video:
 https://vimeo.com/305885893
Code
 shashiongithub/XSum +  additional community code
Data
XSumNEWSROOM