We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS), which targets the use-case of generating actionable instructions from a set of sources. This use-case is different from the use-cases covered in existing multi-document summarization (MDS) datasets and is applicable to educational and industrial scenarios. We employed automatic methods, and leveraged statistics from existing human-crafted qMDS datasets, to create HowSumm from wikiHow website articles and the sources they cite. We describe the creation of the dataset and discuss the unique features that distinguish it from other summarization corpora. Automatic and human evaluations of both extractive and abstractive summarization models on the dataset reveal that there is room for improvement.
@article{arxiv.2110.03179,
title = {HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles},
author = {Odellia Boni and Guy Feigenblat and Guy Lev and Michal Shmueli-Scheuer and Benjamin Sznajder and David Konopnicki},
journal= {arXiv preprint arXiv:2110.03179},
year = {2021}
}
Comments
8 pages, 4 figures, 5 tables. HowSumm dataset is publicly available at \url{https://ibm.biz/BdfhzH}