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 release our data and code in hope that this work will promote advances in summarization in the multi-document setting.
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
A new large-scale Multi-News dataset and an end-to-end summarization model combining extractive and standard document models improve multi-document summarization performance.
- Year
- 2019
- Venue
- multi-news-a-large-scale-multi-document-1
- Authors
- 5
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- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/1906.01749v3ARXIV-DEFAULT
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