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2019 ACL ACL 2019

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

Abstract

AbstractAutomatic 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.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
📈 Trend Setter — Summarization
🧭 Keyword Pioneer — neural encoder-decoder
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors

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

Topics

Machine Learning > Core Methods > Representation Learning Natural Language Processing > Generation > Summarization Natural Language Processing > Applications > Summarization Natural Language Processing > Resources & Methods > Pretraining

Keywords

extractive summarization hierarchical model multi-document summarization encoder-decoder model neural encoder-decoder abstractive model
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