Surfer100: Generating Surveys From Web Resources, Wikipedia-style
Irene Li, Alex Fabbri, Rina Kawamura, Yixin Liu, Xiangru Tang, Jaesung Tae, Chang Shen, Sally Ma, Tomoe Mizutani, Dragomir Radev
Abstract
Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely. As a result, methods for automatically producing content are valuable tools to address this information overload. We show that recent advances in pretrained language modeling can be combined for a two-stage extractive and abstractive approach for Wikipedia lead paragraph generation. We extend this approach to generate longer Wikipedia-style summaries with sections and examine how such methods struggle in this application through detailed studies with 100 reference human-collected surveys. This is the first study on utilizing web resources for long Wikipedia-style summaries to the best of our knowledge.- Anthology ID:
- 2022.lrec-1.576
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- 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, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5388–5392
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.576
- DOI:
- Cite (ACL):
- Irene Li, Alex Fabbri, Rina Kawamura, Yixin Liu, Xiangru Tang, Jaesung Tae, Chang Shen, Sally Ma, Tomoe Mizutani, and Dragomir Radev. 2022. Surfer100: Generating Surveys From Web Resources, Wikipedia-style. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5388–5392, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Surfer100: Generating Surveys From Web Resources, Wikipedia-style (Li et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.lrec-1.576.pdf
- Data
- WikiSum