@inproceedings{wu2021automatic,
author = {Wu, Zeqiu and Galley, Michel and Brockett, Chris and Zhang, Yizhe and Dolan, Bill},
title = {Automatic Document Sketching: Generating Drafts from Analogous Texts},
booktitle = {ACL-IJCNLP 2021},
year = {2021},
month = {May},
abstract = {The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document. However, the high branching factor inherent to text generation impedes the ability of even the strongest language models to offer useful editing suggestions at a more global or document level. We introduce a new task, document sketching, which involves generating entire draft documents for the writer to review and revise. These drafts are built from sets of documents that overlap in form -- sharing large segments of potentially reusable text -- while diverging in content. To support this task, we introduce a Wikipedia-based dataset of analogous documents and investigate the application of weakly supervised methods, including use of a transformer-based mixture of experts, together with reinforcement learning. We report experiments using automated and human evaluation methods and discuss relative merits of these models.},
url = {http://approjects.co.za/?big=en-us/research/publication/automatic-document-sketching-generating-drafts-from-analogous-texts/},
}