Kurosawa: A Script Writer’s Assistant (2023)

Abstract: Storytelling is the lifeline of the entertainment industrymovies, TV shows, and stand-up comedies, all need stories. A good and gripping script is the lifeline of storytelling and demands creativity and resource investment. Good scriptwriters are rare to find and often work under severe time pressure. Consequently, entertainment media are actively looking for automation. In this paper, we present an AIbased script-writing workbench called KUROSAWA which addresses the tasks of plot generation and script generation. Plot generation aims to generate a coherent and creative plot (600–800 words) given a prompt (15–40 words). Script generation, on the other hand, generates a scene (200–500 words) in a screenplay format from a brief description (15–40 words). Kurosawa needs data to train. We use a 4-act structure of storytelling to annotate the plot dataset manually. We create a dataset of 1000 manually annotated plots and their corresponding prompts/storylines and a gold-standard dataset of 1000 scenes with four main elements — scene headings, action lines, dialogues, and character names — tagged individually. We fine-tune GPT-3 with the above datasets to generate plots and scenes. These plots and scenes are first evaluated and then used by the scriptwriters of a large and famous media platform ErosNow. We release the annotated datasets and the models trained on these datasets as a working benchmark for automatic movie plot and script generation.

## introduction movies are one of the most popular sources of entertainment for people worldwide and can be a strong medium for education and social awareness. the impact and influence of film industries can be gauged from the fact that hollywood movies invest 100s of millions of dollars and often make boxoffice collections of billions of dollars. the first motion picture the great train robbery, 1903black & white with no sound-was created at the beginning of the 20th century. since then, the art has gone through several transformations, and now people can instantly access 4k hd movies of their liking on any smart device. throughout the history of film, two of the contributors to a film's blockbuster success have been the quality of its plot and the manner of storytelling. the appeal of the movie decreases drastically if the viewers find the plot drably predictable. writing a creative and exciting script is, therefore, a critical necessity and is extremely challenging. add to this the constraints of time and budget, and the need for (at least partial) automation in script writing becomes obvious. ai-based story generation has been used before. based on the engagement-reflection cognitive explanation of writing, the computer model mex-ica @xcite generates frameworks for short tales. brutus @xcite ) creates short stories with predetermined themes like treachery. with the arrival of pre-trained transformer models, automatic story generation has got a shot in the arm. transformer models like gpt-2 and gpt-3 are extensively used for text generation. these models have shown the capability of generating creative text, albeit sometimes with hallucinations @xcite ). text generated by these models also sometimes lacks coherence and cohesiveness. on the other hand, template-based models can generate coherent text but lack creativity in generating new characters and events in the plot @xcite . the process of creating a movie generally starts with an idea which is then used to create a plot which is used as the base to build the movie script (figure 1 ). novel datasets are an important feature of this paper. we closely studied the plots and prompts of movies from bollywood and hollywood. such plots and prompts were scraped from wikipedia foot_0 and imdb foot_1 , respectively. the plots are then annotated using the 4-act story structure-an extension of the well-known 3-act structure @xcite . the 4-act structure and the annotation methods are explained in detail in appendix a.5 and section 4, respectively. we introduce a dataset of 1000 hollywood movie scenes and their short descriptions. the scripts are scraped from imsdb foot_2 . the scenes are annotated with the four major components of a screenplay: sluglines, action lines, character names and dialogues, described in details in appendix a.4 we introduce a workbench which we call "kurosawa", consisting of datasets and a pair of gpt-3 @xcite ) models fine-tuned with the said datasets. one gpt-3 model generates a movie plot given a short description of the storyline (15-40 words), while the other creates a scene based on a short description of the required scene. importantly, we have provided the "kurosawa" platform to one of the biggest media platforms engaged in the business of making movies and tv shows, producing music and soundtrack etc.-to help script and content writers from different film industries create new movie plots. our contributions in this work are as follows: ## motivation movies are a form of visual media and can have a huge influence on life and society. movie scripts are often 30,000 words long, comparable to a 100page book. though scripts can be diverse, they have fixed and oft-repeated structures, e.g., scene heading, transition, character name, etc.. this fixity and repetition can be dull and time-consuming and can be handed over to ai. however, a surprising fact is that ai-based models can be creative in generating novel characters and stories. these reasons have motivated the film industry to seriously consider harnessing ai for various aspects of movie making, script and scene writing being one of them. los angeles times, 19 december 2022, asks, "ai is here, and it's making movies. is hollywood ready?". the newspaper edition reports mainly movie editing efforts ongoing at various places using ai. our task in the paper is allied but different in the sense that we aim to provide a "script-writers' assistant". ## dataset for movie plot generation, we have taken the plots from wikipedia. the prompts for this task have been taken from imdb. in imdb, this prompt can be of two types. the first is a short description (15-40 words) of the movie, while the second is a long storyline, which varies from 30-200 words and contains much more details about the different characters and events of the movie. we have also collected the genres of each film from imdb. we then divide the plots using a 4-act structure. for scene generation, we take the scripts from imsdb and annotate them with the key elements of a scene. ## experiments and evaluation we fine-tune gpt3 with our datasets (refer appendix a.6). ## results and analysis we present our observations and evaluations. the nature of our task makes human evaluation take precedence over automatic evaluation (it is for automatic movie script generation, after all!). the qualitative analysis of our generated plots and scenes is based on feedback from 5 professional scriptwriters of our industry partner, the well-known media platform. ## conclusion and future work in this paper, we have reported a first-of-its-kind work on automatic plot and script generation from prompts. automatic evaluation, human rating using the likert scale, and qualitative observations by professional scriptwriters from our industry partner (a large and well-reputed media platform)-all vindicate the power of our rich dataset and gpt3 in script generation. we hope our work will help television show writers, game show writers, and so on. there are several future directions: (i) the imbalance in the bollywood plot dataset needs to be rectified; (ii) there is a lot of variation in indian script because of multilingualism, which needs addressing; (iii) the most obvious weakness of gpt-3 is not being able to handle factual data and numbers, causing hallucination and preventing the automatic generation of documentaries and biographies. detection and resolution of hallucination is anyway a growing need for language models.