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Large-scale pretrained language models have shown thrilling generationcapabilities, especially when they generate consistent long text in thousandsof words with ease. However, users of these models can only control the prefixof sentences or certain global aspects of generated text. It is challenging tosimultaneously achieve fine-grained controllability and preserve thestate-of-the-art unconditional text generation capability. In this paper, wefirst propose a new task named "Outline to Story" (O2S) as a test bed forfine-grained controllable generation of long text, which generates amulti-paragraph story from cascaded events, i.e. a sequence of outline eventsthat guide subsequent paragraph generation. We then create dedicate datasetsfor future benchmarks, built by state-of-the-art keyword extraction techniques.Finally, we propose an extremely simple yet strong baseline method for the O2Stask, which fine tunes pre-trained language models on augmented sequences ofoutline-story pairs with simple language modeling objective. Our method doesnot introduce any new parameters or perform any architecture modification,except several special tokens as delimiters to build augmented sequences.Extensive experiments on various datasets demonstrate state-of-the-artconditional story generation performance with our model, achieving betterfine-grained controllability and user flexibility. Our paper is among the firstones by our knowledge to propose a model and to create datasets for the task of"outline to story". Our work also instantiates research interest offine-grained controllable generation of open-domain long text, wherecontrolling inputs are represented by short text.

Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events