Automatic Document Sketching: Generating Drafts from Analogous Texts

• URL: http://arxiv.org/abs/2106.07192v1
• Date: Mon, 14 Jun 2021 06:46:06 GMT
• Title: Automatic Document Sketching: Generating Drafts from Analogous Texts
• Authors: Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Bill Dolan
• Abstract summary: 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. We investigate the application of weakly supervised methods, including use of a transformer-based mixture of experts, together with reinforcement learning.
• Score: 44.626645471195495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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