HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
This dataset addresses the problem of generating actionable instructions from multiple documents for educational and industrial applications, but it is incremental as it builds on existing summarization tasks with a new use-case.
The authors introduced HowSumm, a new large-scale dataset for query-focused multi-document summarization designed to generate actionable instructions from sources, addressing a gap in existing datasets for educational and industrial use. Evaluations showed that current summarization models have room for improvement on this dataset.
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.