---
title: "Newsroom: A Dataset of 1.3 Million Summaries — Grusky 2018"
description: "Automatic summarization lacks large, high-quality data for training and evaluation."
authors: ["Max Grusky", "Mor Naaman", "Yoav Artzi"]
year: 2018
venue: "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)"
source: "https://aclanthology.org/N18-1065/"
doi: "10.18653/v1/n18-1065"
arxiv: "1804.11283"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/grusky-2018-newsroom-dataset-1-3-million/"
---
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# Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies

**Authors:** Max Grusky, Mor Naaman, [Yoav Artzi](https://md.research.elamur.ai/free/en/authors/yoav-artzi/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N18-1065/) · [PDF](https://aclanthology.org/N18-1065.pdf) · [DOI 10.18653/v1/n18-1065](https://doi.org/10.18653/v1/n18-1065) · [arXiv 1804.11283](https://arxiv.org/abs/1804.11283) · [OpenAlex](https://openalex.org/W2799064010)

**Citations:** 479 citations (openalex-snapshot · 2026-10-14) · [299](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Automatic summarization lacks large, high-quality data for training and evaluation. Earlier resources use stand-ins such as headlines, concatenated bullet points, or librarian-written abstracts, and each reflects a narrow style. The authors built NEWSROOM, which pairs 1.3 million news articles with summaries that newsroom authors and editors wrote for search engines and social media. They crawled over 100 million archived pages via Archive.org from 38 major English-language publishers, covering 1998–2017. The dataset is distributed as a list of immutable archive URLs.

The authors also define measures of how extractive a summary is. They use these measures to split the corpus into extractive, mixed, and abstractive subsets and to compare it with DUC, Gigaword, the New York Times Corpus, and CNN / Daily Mail. Baselines were tested with ROUGE and with a new crowdsourced human evaluation protocol. The simple Lede-3 baseline proved very hard to beat, so the dataset remains an open challenge. Training a pointer-generator on NEWSROOM also improved results on out-of-domain data.

## Key points
- NEWSROOM is described as the largest summarization dataset to date: 1.3M article–summary pairs from 38 publishers, available at summari.es.
- The summaries come from HTML metadata and mix abstractive and extractive strategies to varying degrees. Proposed measures (including coverage and compression) quantify this diversity.
- The evaluated systems were Lede-3, an extractive oracle (Fragments), TextRank, Abs-N, and the pointer-generator variants Pointer-C, Pointer-S and Pointer-N. Pointer-N, trained on NEWSROOM, scored best among models on every dataset except CNN / Daily Mail.
- ROUGE-1, ROUGE-2 and ROUGE-L F1 were computed with ROUGE v1.5.5, using text tokenized by the Stanford CoreNLP tokenizer.
- In the human evaluation, Amazon Mechanical Turk raters judged 60 summaries (20 per subset, 3 raters each) on informativeness, relevance, fluency and coherence. Lede-3 averaged 4.08, TextRank 3.81 and Pointer-S 3.73. The Fragments oracle scored only 3.08.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

299 papers of our library cite it (of the 140884 whose bibliographies we read); 23 of them have a page here, 276 not yet.

- [Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/aclbulk-2026-dahan-leveraging-digitized-newspapers-collect-summarization/)
- [LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks](https://md.research.elamur.ai/free/en/papers/bavaresco-2024-llms-instead-human-judges-large/)
- [TLDR: Extreme Summarization of Scientific Documents](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/)
- [SummScreen: A Dataset for Abstractive Screenplay Summarization](https://md.research.elamur.ai/free/en/papers/chen-2022-summscreen-dataset-abstractive-screenplay-summarization/)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/)
- [Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://md.research.elamur.ai/free/en/papers/fabbri-2019-multi-news-large-scale-multi/)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://md.research.elamur.ai/free/en/papers/he-2022-ctrlsum-generic-controllable-text-summarization/)
- [Efficient Attentions for Long Document Summarization](https://md.research.elamur.ai/free/en/papers/huang-2021-efficient-attentions-long-document-summarization/)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
- [Neural Text Summarization: A Critical Evaluation](https://md.research.elamur.ai/free/en/papers/kryscinski-2019-neural-text-summarization-critical-evaluation/)
- [Hierarchical Transformers for Multi-Document Summarization](https://md.research.elamur.ai/free/en/papers/liu-2019-hierarchical-transformers-multi-document-summarization/)
- [Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
- [Entity-level Factual Consistency of Abstractive Text Summarization](https://md.research.elamur.ai/free/en/papers/nan-2021-entity-level-factual-consistency-abstractive/)
- [On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://md.research.elamur.ai/free/en/papers/pilault-2020-extractive-abstractive-neural-document-summarization/)
- [MLSUM: The Multilingual Summarization Corpus](https://md.research.elamur.ai/free/en/papers/scialom-2020-mlsum-multilingual-summarization-corpus/)
- [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-nlg-evaluator-preliminary/)
- [BARTScore: Evaluating Generated Text as Text Generation](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://md.research.elamur.ai/free/en/papers/zhao-2019-moverscore-text-generation-evaluating-contextualized/)
- [DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence](https://md.research.elamur.ai/free/en/papers/zhao-2023-discoscore-evaluating-text-generation-bert/)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://md.research.elamur.ai/free/en/papers/zhong-2021-qmsum-new-benchmark-query-based/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{grusky2018newsroom,
  title = {{Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies}},
  author = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-1065},
  eprint = {1804.11283},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n18-1065}
}
```

- [← previous Neural Surface Realization for Italian](https://md.research.elamur.ai/free/en/papers/basile-2018-neural-surface-realization-italian/)
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- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/3/)

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