摘要：Some types of documents need to meet size constraints, such as fitting into a limited number of pages. This can be a difficult constraint to enforce in a pipelined natural language generation (NLG) system, because size is mostly determined by content decisions, which usually are made at the beginning of the pipeline, but size cannot be accurately measured until the document has been completely processed by the NLG system. I present experimental data on the performance of single-solution pipeline, multiple-solution pipeline, and revision-based variants of the STOP system (which produces personalized smoking-cessation leaflets) in meeting a size constraint. This shows that a multiple-solution pipeline does much better than a single-solution pipeline, and that a revision-based system does best of all.
