The paper introduces a new method for text generation, named Copy-Over-Generate (COG), which differs from traditional approaches that generate words from a fixed vocabulary. Instead, COG progressively copies phrases from a massive text collection, aiming to generate coherent text continuations through multiple rounds of phrase retrieval.

COG stands on the line of retrieval-augmented text generation research but takes a radical step forward. Unlike previous work that combines retrieval and generation, in COG, retrieval is generation.

COG shares some ideas with previous work such as replacing the fixed vocabulary with a nonparametric phrase table.

The paper presents experimental results showing the advantages of COG over strong baselines in three experimental settings: standard language modeling (using the WikiText-103 dataset), domain adaptation (using the Law-MT dataset), and an enlarged phrase index (using the En-Wiki dataset).

Despite the promising results, the authors acknowledge that there are some flaws in the COG method. For example, COG may copy a phrase that is incoherent with the previously copied phrase, or it may only copy a part of a complete phrase, leading to inaccurate generation results.