Document Level Revision

Document-level revision focuses on automatically improving entire documents, going beyond sentence-level corrections to address higher-level issues like coherence, consistency, and overall structure. Current research emphasizes developing large, annotated datasets of revised documents to train and evaluate models, often leveraging techniques from natural language processing (NLP) such as pre-trained language models fine-tuned for specific revision tasks. This work is significant because it aims to automate complex editing processes, potentially assisting researchers and writers in improving the quality and clarity of their work across various domains, from scientific publications to general writing.