Abstract
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.- Anthology ID:
- 2020.lrec-1.820
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6645–6650
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.820
- DOI:
- Cite (ACL):
- Diego Antognini and Boi Faltings. 2020. GameWikiSum: a Novel Large Multi-Document Summarization Dataset. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6645–6650, Marseille, France. European Language Resources Association.
- Cite (Informal):
- GameWikiSum: a Novel Large Multi-Document Summarization Dataset (Antognini & Faltings, LREC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.820.pdf
- Code
- Diego999/GameWikiSum
- Data
- GameWikiSum, WikiSum