Hello and thank you for taking the time to visit NewsEdits!
This is the repository for the Outstanding Paper in NAACL2022, NewsEdits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge.
In this repo, we:
- Provide our preprocessed datasets of NewsEdits. The data can be found at the following Google Drive url. We provide the following sources:
- Associated Press (finished)
- BBC (finished)
- Guardian (finished)
- Independent (finished)
- New York Times (finished)
- Washington Post (finished)
- Reuters (finished)
- Calgary Herald (finished)
- Canadaland (finished)
- Canadian Broadcast Corporation (finished)
- Dailymail (finished)
- Fox (finished)
- Globemail (finished)
- La Presse (finished)
- National Post (finished)
- Telegraph (finished)
- Toronto Star (finished)
- The Rebel (matched, no sentence-splitting. Finishing ASAP.)
- Toronto Sun (matched, no sentence-splitting. Finishing ASAP.)
- CNN (not started)
- Provide demo notebooks to load and visualize the data in the
demo_notebooks/folder. - Provide Spark scripts to process new datasets in the
spark/folder. - Provide model code (to come).
We are working hard to put the final touches on this repo and we hope in the next weeks to be finished. Please reach out to spangher@usc.edu with any issues, questions, data requests, etc. and we will prioritise your enquiries.
If you've found any of this useful or inspiring, please
@inproceedings{spangher-etal-2022-newsedits,
title = "{N}ews{E}dits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge",
author = "Spangher, Alexander and
Ren, Xiang and
May, Jonathan and
Peng, Nanyun",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.10",
pages = "127--157",
abstract = "News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021).We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high-accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses showing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are predictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news.",
}