Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model

Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, Dragomir Radev


Abstract
Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets. However, multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. In this paper, we introduce Multi-News, the first large-scale MDS news dataset. Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. We benchmark several methods on Multi-News and hope that this work will promote advances in summarization in the multi-document setting.
Anthology ID:
P19-1102
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1074–1084
Language:
URL:
https://www.aclweb.org/anthology/P19-1102
DOI:
10.18653/v1/P19-1102
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1102.pdf
Video:
 https://vimeo.com/384478403
Presentation:
 P19-1102.Presentation.pdf