Generating alternate endings for short stories

Generative artificial intelligence (AI) is being used to generate all kinds of digital data: images, speech, text, and so on. with high fidelity. Within natural language processing, generative AI has many interesting applications such as summarization and story generation. In this article, we focus on generating alternate endings for short stories. Although some work has been done on story generation, to the best of our knowledge, very little work has been done on generating alternate endings. For a more precise ending, a large amount of context needs to be provided making story ending generation a more challenging problem than complete story generation. We propose a complete pipeline consisting of text understanding and text generation modules for generating story endings. Within story understanding modules, we propose and integrate various sub-modules to capture the context of the story. This context is combined with the original story during the training and story generation phase for which a T5 transformer is used. Compared to previous studies, our proposed model reports a higher BLEU value. We also conducted a subjective user study to evaluate the endings generated by our model. Machine-generate endings were given an average score of 3.82 (out of 5) as opposed to 3.86 for human-generated endings.