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A story generator or plot generator is a tool that generates basic narratives or plot ideas. The generator could be in the form of a computer program, a chart with multiple columns, a book composed of panels that flip independently of one another, or a set of several adjacent reels that spin independently of one another, allowing a user to select elements of a narrative plot.
Hooked is a freemium smartphone app that allows users to write or read short stories made up of text messages between characters. [1] [2] CEO Prerna Gupta described the app as "books for the Snapchat generation" or "Twitter for fiction." [3] As of March 2019, the app had more than 40 million active users. [4]
Storyland is a browser-based narrative work of electronic literature.The project is included in the first Electronic Literature Collection. [1] It was created by Nanette Wylde in 2000 and is considered a form of Combinatory Narrative or Generative Poetry which is created with the use of the computer's random function.
An outline, also called a hierarchical outline, is a list arranged to show hierarchical relationships and is a type of tree structure. An outline is used [1] to present the main points (in sentences) or topics of a given subject. Each item in an outline may be divided into additional sub-items.
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Short story - A work of at least 2,000 words but under 7,500 words (between about 10 and 40 pages). Novella - A work of at least 17,500 words but under 50,000 words (90-170 pages). [6] The boundary between a long short story and a novella is vague. [7] Novel - A work of 50,000 words or more (about 170+ pages). Epic - A long poem.
A step outline (also informally called a beat sheet or scene-by-scene [1]) is a detailed telling of a story with the intention of turning the story into a screenplay for a motion picture. The step outline briefly details every scene of the screenplay's story, and often has indications for dialogue and character interactions.
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.