Existing Natural Language Generation ( ) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity. E ective solutions or, at least evaluations of modern paradigms for such creative tasks have been elusive, unfortunately. is 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 ( ), 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 ( SMT) and syntax based SMT ( SMT) to 'translate' the incoherent input text into stories. We then implement a deep recurrent neural network ( ) architecture that encodes sequence of variable length input descriptions to corresponding latent representations and decodes them to produce well formed comprehensive story like summaries. e e cacy of the suggested approaches is demonstrated on a publicly available dataset with the help of popular machine translation and summarization evaluation metrics. We believe, a system like ours has di erent interesting applications-for example, creating news articles from phrases of event information. •Computing methodologies → Natural language generation; Supervised learning; Neural networks; KEYWORDS Story, Natural Language Generation, Sequential Learning ACM Reference format: {pajain06,priyanka.agrawal,abhijimi,mosukhwa,anirlaha,kartsank} Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s
REF explores story generation via statistical machine translation models.
Story Generation from Sequence of Independent Short Descriptions
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Computer Science