Story Generation from Sequence of Independent Short Descriptions

By Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, Karthik Sankaranarayanan. 2017

91 citations recorded by the source corpus.

Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity. Effective solutions or, at least evaluations of modern NLG paradigms for such creative tasks have been elusive, unfortunately. This paper introduces and addresses the task of coherent story generation from independent descriptions, describing a scene or an event. Towards this, we explore along two popular text-generation paradigms -- (1) Statistical Machine Translation (SMT), posing story generation as a translation problem and (2) Deep Learning, posing story generation as a sequence to sequence learning problem. In SMT, we chose two popular methods such as phrase based SMT (PB-SMT) and syntax based SMT (SYNTAX-SMT) to `translate' the incoherent input text into stories. We then implement a deep recurrent neural network (RNN) architecture that encodes sequence of variable length input descriptions to corresponding latent representations and decodes them to produce well formed comprehensive story like summaries. The efficacy of the suggested approaches is demonstrated on a publicly available dataset with the help of popular machine translation and summarization evaluation metrics.

Subfields: Natural Language Processing

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Citations in the corpus

Hierarchical Neural Story Generation (2018, background): “For instance, Kiros et al. (2015) uses photos to inspire short paragraphs trained on romance novels, and Jain et al. (2017) chain a series of independent descriptions together into a short story.”
Plan-And-Write: Towards Better Automatic Storytelling (2018, background): “Jain et al. [2017] explore generating coherent stories from independent textual descriptions based on two conditional text-generation methods: statistical machine translation and sequence-to-sequence models.”
Text Editing by Command (2020, background): “The task of Document Generation considered in our work bears similarity with work on generating long-form narratives (Jain et al., 2017).”
Strategies for Structuring Story Generation (2019, background): “Recent work has extended this to modelling story events (Martin et al., 2017; Mostafazadeh et al., 2016), plot graphs (Li et al., 2013), or generate by conditioning upon sequences of images (Huang et al., 2016) or descriptions (Jain et al., 2017).”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, methodology): “[207] used RNN variants with attention to produce short stories from”
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (2020, background): “Neural Story Generation Many existing neural story generation models generated stories by conditioning upon various contents such as images (Huang et al., 2016) and short text descriptions (Jain et al., 2017).”
Learning to Control the Fine-grained Sentiment for Story Ending Generation (2019, background): “Recently, many approaches are proposed to generate a better story in terms of coherence (Jain et al., 2017; Xu et al., 2018), rationality (Li et al., 2018), topic-consistence (Yao et al., 2018a).”