Automatic Document Sketching: Generating Drafts from Analogous Texts

By Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Bill Dolan. 2021

7 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Natural Language Generation

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Citations in the corpus

PEER: A Collaborative Language Model (2022, background): “…of writing coherent pieces of text by iteratively performing small updates has some similarity with recent approaches like chain-of-thought prompting (Wei et al., 2022b; Dohan et al., 2022) and document sketching (Wu et al., 2021), that also break down a complex task into multiple smaller steps.”
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2021, result): “Previous work (Wu et al., 2021) also had a similar observation that initializing with a less trained model leads to more stable RL training.”