WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation
Nachshon Cohen, Oren Kalinsky, Yftah Ziser, Alessandro Moschitti
Abstract
Recent works made significant advances on summarization tasks, facilitated by summarization datasets. Several existing datasets have the form of coherent-paragraph summaries. However, these datasets were curated from academic documents that were written for experts, thus making the essential step of assessing the summarization output through human-evaluation very demanding. To overcome these limitations, we present a dataset based on article summaries appearing on the WikiHow website, composed of how-to articles and coherent-paragraph summaries written in plain language. We compare our dataset attributes to existing ones, including readability and world-knowledge, showing our dataset makes human evaluation significantly easier and thus, more effective. A human evaluation conducted on PubMed and the proposed dataset reinforces our findings.
BibTeX
@inproceedings{cohen-etal-2021-wikisum,
title = "{W}iki{S}um: Coherent Summarization Dataset for Efficient Human-Evaluation",
author = "Cohen, Nachshon and
Kalinsky, Oren and
Ziser, Yftah and
Moschitti, Alessandro",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.28/",
doi = "10.18653/v1/2021.acl-short.28",
pages = "212--219"
}