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The Story Of Computational Narratology


Goal of Narrative Generation is to aid humans in authoring improved narrations or produce automated better narrations. It has far-reaching applications in the field of Medicine, Education, Literature and Journalism. A narrative generated from the vital parameters of a patient is known to aid better decision making when compared to information in tabular and graphical data. In Portet et al. [18], an attempt has been made to generate narratives from neonatal intensive care data. A narrative about a patient’s condition is easy to comprehend, visualize and triggers quick decision to offer appropriate medication to the patient.

In schools for children with special communication needs, narratives about what such children truly want to express can be constructed using narrative generation. In Tintarev et al. [25], a system is proposed that automatically creates a personal narrative from sensor data and other media (photos and audio) which can be used by children with complex communication needs in schools to support interactive narrative about personal experiences.

Screen writers in commercial entertainment can use diverse coherent narratives generated from a corpus as source of inspiration to author novel stories. In Lönneker et al. [14], models and approaches have been proposed for automated creation of fiction or “literary artifacts” that might take the form of prose, poetry or drama. Models achieve combination of events and generation of plots which are difficult for a human author to conceive and resulting narratives can be a trigger for the author to think and pursue a new story or a fundamentally different plot to convey a story.

In the field of journalism, an apt technical narration of facts makes a large difference in getting news to proliferate. Often news is backed by statistical data and there is a need to form a narrative out of it for readers to make a quick sense of the news the data is meant to convey. In Dörr [6], a study has been done at a technical level, to address whether natural language generation can produce news. The study also addresses the economic potential of NLG in journalism as well as indicating its institutionalization on an organizational level.

One of the earliest story generation system was TALE-SPIN [3] in 1981. In this system, goals were set for the characters and the recorded path of characters constituted the generated narrative. TALE-SPIN was a pioneer in NLG, and its approach for problem-solving, became the de facto standard model for other AI researchers working on storytelling and related areas [28].

2.1 Narrative Generation Systems

Some of the prominent story-generation systems in recent times, are BRUTUS [5], MINSTREL [26] and MEXICA [27]. y Pérez and Sharples [28] provides a detailed evaluation of these three systems.

BRUTUS is a Prolog-based system which generates stories on predefined themes. The themes are primarily of betrayal, self-deception and other literary themes. At a high-level, there are three processes that characterize this system: instantiation, development of plot, and expansion of story grammar involved in story generation. Instantiation is achieved through story-frames or story-themes. Story-frame captures the characters and their goals and story-theme sets the theme of the story. The plot generation is achieved through pro-active actions or actions as per the plan and reactive actions by the characters and the story generation stops when there are no actions that can be generated. The final output is generated through story-grammar expansion where lexical constructs are chosen and text is generated. BRUTUS is the recent of the three systems and reuses known methodologies.

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