# GameWikiSum: a Novel Large Multi-Document Summarization Dataset

arXiv: https://arxiv.org/abs/2002.06851 · HTML: https://mlsanity.com/paper/2002.06851/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [AgreeSum: Agreement-Oriented Multi-Document Summarization](https://mlsanity.com/paper/2106.02278/) (2021)
- [WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs](https://mlsanity.com/paper/2209.13101/) (2022)
- [ALIGNMEET: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation](https://mlsanity.com/paper/2205.05433/) (2022)
- [PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT](https://mlsanity.com/paper/2305.11536/) (2023)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents](https://mlsanity.com/paper/2412.11814/) (2024)
- [Unsupervised Multi-document Summarization with Holistic Inference](https://mlsanity.com/paper/2309.04087/) (2023)
- [XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages](https://mlsanity.com/paper/2303.12308/) (2023)

## Abstract (arXiv)

Today's research progress in the field of multi-document summarization is obstructed by the small number of available datasets. Since the acquisition of reference summaries is costly, existing datasets contain only hundreds of samples at most, resulting in heavy reliance on hand-crafted features or necessitating additional, manually annotated data. The lack of large corpora therefore hinders the development of sophisticated models. Additionally, most publicly available multi-document summarization corpora are in the news domain, and no analogous dataset exists in the video game domain. In this paper, we propose GameWikiSum, a new domain-specific dataset for multi-document summarization, which is one hundred times larger than commonly used datasets, and in another domain than news. Input documents consist of long professional video game reviews as well as references of their gameplay sections in Wikipedia pages. We analyze the proposed dataset and show that both abstractive and extractive models can be trained on it. We release GameWikiSum for further research: https://github.com/Diego999/GameWikiSum.