Narrative prose generation

Callaway, Charles; Lester, James C.

doi:10.1016/s0004-3702(02)00230-8

Cited by 118 publications

(69 citation statements)

References 10 publications

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“…Narrator seem to be a system that intends to continue the recent trend appeared in the NLG field for producing narratives which is pioneered by systems like ProtoPropp [35], PRINCE [38] and StoryBook [19]. Rhetorical relation types used in Narrator includes Cause, Contrast, Temporal, Purpose and Elaboration.…”

Section: Document Structuring Using Rhetorical Structuresmentioning

confidence: 99%

Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

Perera

Nand

2017

cai

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Abstract. Natural Language Generation (NLG) is defined as the systematic approach for producing human understandable natural language text based on nontextual data or from meaning representations. This is a significant area which empowers human-computer interaction. It has also given rise to a variety of theoretical as well as empirical approaches. This paper intends to provide a detailed overview and a classification of the state-of-the-art approaches in Natural Language Generation. The paper explores NLG architectures and tasks classed under document planning, micro-planning and surface realization modules. Additionally, this paper also identifies the gaps existing in the NLG research which require further work in order to make NLG a widely usable technology.

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Section: Document Structuring Using Rhetorical Structuresmentioning

confidence: 99%

Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

Perera

Nand

2017

cai

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“…Most of this research has focused on high-level constructs such as plots and character development [6], although some work has also been done on lower-level linguistic issues [33]. Since the stories are fi the story generator is typically either free to make up content for the story, or to select 'interesting' content from a knowledgebase that describes the fi world.…”

Section: Narrative In Nlgmentioning

confidence: 99%

Personal storytelling: Using Natural Language Generation for children with complex communication needs, in the wild…

Tintarev

Reiter

Black

et al. 2016

International Journal of Human-Computer Studies

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This paper describes a Natural Language Generation system (NLG), How was School Today? that automatically creates a personal narrative from sensor data and other media (photos and audio). It can be used by children with complex communication needs in schools to support interactive narrative about personal experiences. The robustness of story generation to missing data was identified as a key area for improvement in a feasibility study of the system at a fi special needs school. This paper therefore suggests three possible methods for generating stories from unstructured data: clustering by voice recording, by location, or by time. Clustering based on voice recordings resulted in stories that were perceived as most easy to read, and to make most sense, by parents in a quantitative evaluation. This method was implemented in the live system, which was developed and evaluated iteratively at a second special needs school with children with diff t usage profi Open challenges and possibilities for nlg in augmented and alternative communication are also discussed.

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“…It will be important to analyse literary texts in their full complexity before we are capable of pronouncing on the use of time. This will no doubt be aided by research on narrative generation, such as (Callaway and Lester, 2002), (Riedl andYoung, 2012), and(Montfort, 2007), where temporal representations at the abstract level are made use of, but this must be complemented by empirical work on actual texts.…”

Section: Time and Textmentioning

confidence: 99%

Time after Time: Representing Time in Literary Texts

Levison¹,

Lessard²

2014

Proceedings of the 3rd Workshop on Computational Linguistics for Literature (CLFL)

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The representation of temporal information in text represents a significant computational challenge. The problem is particularly acute in the case of literary texts, which tend to be massively underspecified, often relying on a network of semantic relations to establish times and timings. This paper shows how a model based on threaded directed acyclic graphs makes it possible to capture a range of subtle temporal information in this type of text and argues for an onomasiological approach which places meaning before form.

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Narrative prose generation

Cited by 118 publications

References 10 publications

Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

Personal storytelling: Using Natural Language Generation for children with complex communication needs, in the wild…

Time after Time: Representing Time in Literary Texts

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