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
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5388–5392
Language:
URL:
https://aclanthology.org/2022.lrec-1.576
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.576.pdf
Data
WikiSum