@inproceedings{li-etal-2022-surfer100,
title = "Surfer100: Generating Surveys From Web Resources, {W}ikipedia-style",
author = "Li, Irene and
Fabbri, Alex and
Kawamura, Rina and
Liu, Yixin and
Tang, Xiangru and
Tae, Jaesung and
Shen, Chang and
Ma, Sally and
Mizutani, Tomoe and
Radev, Dragomir",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.576",
pages = "5388--5392",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Surfer100: Generating Surveys From Web Resources, Wikipedia-style
%A Li, Irene
%A Fabbri, Alex
%A Kawamura, Rina
%A Liu, Yixin
%A Tang, Xiangru
%A Tae, Jaesung
%A Shen, Chang
%A Ma, Sally
%A Mizutani, Tomoe
%A Radev, Dragomir
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F li-etal-2022-surfer100
%X 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.
%U https://aclanthology.org/2022.lrec-1.576
%P 5388-5392
Markdown (Informal)
[Surfer100: Generating Surveys From Web Resources, Wikipedia-style](https://aclanthology.org/2022.lrec-1.576) (Li et al., LREC 2022)
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.