# HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles

arXiv: https://arxiv.org/abs/2110.03179 · HTML: https://mlsanity.com/paper/2110.03179/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [WikiHow: A Large Scale Text Summarization Dataset](https://mlsanity.com/paper/1810.09305/) (2018)
- [Hierarchical Transformers for Multi-Document Summarization](https://mlsanity.com/paper/1905.13164/) (2019)
- [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://mlsanity.com/paper/2010.14235/) (2020)
- [A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal](https://mlsanity.com/paper/2005.10070/) (2020)
- [AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization](https://mlsanity.com/paper/2010.12694/) (2020)
- [orgFAQ: A New Dataset and Analysis on Organizational FAQs and User Questions](https://mlsanity.com/paper/2009.01460/) (2020)

## Built on by (most cited first, 8 of 8)

- [Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP](https://mlsanity.com/paper/2407.00402/) (2024)
- [WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations](https://mlsanity.com/paper/2403.01774/) (2024)
- [ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation](https://mlsanity.com/paper/2410.15511/) (2024)
- [MAVIS: A Benchmark for Multimodal Source Attribution in Long-form Visual Question Answering](https://mlsanity.com/paper/2511.12142/) (2025)
- [SurveySum: A Dataset for Summarizing Multiple Scientific Articles into a Survey Section](https://mlsanity.com/paper/2408.16444/) (2024)
- [InstructNet: A Novel Approach for Multi-Label Instruction Classification through Advanced Deep Learning](https://mlsanity.com/paper/2512.18301/) (2025)
- [Instructional Text Across Disciplines: A Survey of Representations, Downstream Tasks, and Open Challenges Toward Capable AI Agents](https://mlsanity.com/paper/2410.18529/) (2024)
- [Investigating Consistency in Query-Based Meeting Summarization: A Comparative Study of Different Embedding Methods](https://mlsanity.com/paper/2402.06907/) (2024)

## Abstract (arXiv)

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.