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

## TL;DR

This paper introduces Multi-News, a large-scale dataset for multi-document news summarization, and proposes an end-to-end hierarchical model that combines extractive and abstractive techniques, achieving competitive results.

## Contribution

The paper provides the first large-scale multi-document summarization dataset and a novel hierarchical model that enhances multi-news summarization performance.

## Key findings

- Multi-News dataset enables large-scale training for MDS.
- The hierarchical model outperforms baseline methods.
- Benchmark results promote future research in multi-document summarization.

## 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 release our data and code in hope that this work will promote advances in summarization in the multi-document setting.