Screenplay Quality Assessment: Can We Predict Who Gets Nominated?

• URL: http://arxiv.org/abs/2005.06123v1
• Date: Wed, 13 May 2020 02:39:56 GMT
• Title: Screenplay Quality Assessment: Can We Predict Who Gets Nominated?
• Authors: Ming-Chang Chiu, Tiantian Feng, Xiang Ren, Shrikanth Narayanan
• Abstract summary: We present a method to evaluate the quality of a screenplay based on linguistic cues. Based on industry opinions and narratology, we extract and integrate domain-specific features into common classification techniques.
• Score: 53.9153892362629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deciding which scripts to turn into movies is a costly and time-consuming process for filmmakers. Thus, building a tool to aid script selection, an initial phase in movie production, can be very beneficial. Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues. We address this in a two-fold approach: (1) we define the task as predicting nominations of scripts at major film awards with the hypothesis that the peer-recognized scripts should have a greater chance to succeed. (2) based on industry opinions and narratology, we extract and integrate domain-specific features into common classification techniques. We face two challenges (1) scripts are much longer than other document datasets (2) nominated scripts are limited and thus difficult to collect. However, with narratology-inspired modeling and domain features, our approach offers clear improvements over strong baselines. Our work provides a new approach for future work in screenplay analysis.

Related papers

• ScreenWriter: Automatic Screenplay Generation and Movie Summarisation [55.20132267309382]
  Video content has driven demand for textual descriptions or summaries that allow users to recall key plot points or get an overview without watching. We propose the task of automatic screenplay generation, and a method, ScreenWriter, that operates only on video and produces output which includes dialogue, speaker names, scene breaks, and visual descriptions. ScreenWriter introduces a novel algorithm to segment the video into scenes based on the sequence of visual vectors, and a novel method for the challenging problem of determining character names, based on a database of actors' faces.
  arXiv  Detail & Related papers  (2024-10-17T07:59:54Z)
• MovieSum: An Abstractive Summarization Dataset for Movie Screenplays [11.318175666743656]
  We present a new dataset, MovieSum, for abstractive summarization of movie screenplays. This dataset comprises 2200 movie screenplays accompanied by their Wikipedia plot summaries.
  arXiv  Detail & Related papers  (2024-08-12T16:43:09Z)
• Movie101v2: Improved Movie Narration Benchmark [53.54176725112229]
  Automatic movie narration aims to generate video-aligned plot descriptions to assist visually impaired audiences. We introduce Movie101v2, a large-scale, bilingual dataset with enhanced data quality specifically designed for movie narration. Based on our new benchmark, we baseline a range of large vision-language models, including GPT-4V, and conduct an in-depth analysis of the challenges in narration generation.
  arXiv  Detail & Related papers  (2024-04-20T13:15:27Z)
• Select and Summarize: Scene Saliency for Movie Script Summarization [11.318175666743656]
  We introduce a scene saliency dataset that consists of human-annotated salient scenes for 100 movies. We propose a two-stage abstractive summarization approach which first identifies the salient scenes in script and then generates a summary using only those scenes.
  arXiv  Detail & Related papers  (2024-04-04T16:16:53Z)
• Movie101: A New Movie Understanding Benchmark [47.24519006577205]
  We construct a large-scale Chinese movie benchmark, named Movie101. We propose a new metric called Movie Narration Score (MNScore) for movie narrating evaluation. For both two tasks, our proposed methods well leverage external knowledge and outperform carefully designed baselines.
  arXiv  Detail & Related papers  (2023-05-20T08:43:51Z)
• VScript: Controllable Script Generation with Audio-Visual Presentation [56.17400243061659]
  VScript is a controllable pipeline that generates complete scripts including dialogues and scene descriptions. We adopt a hierarchical structure, which generates the plot, then the script and its audio-visual presentation. Experiment results show that our approach outperforms the baselines on both automatic and human evaluations.
  arXiv  Detail & Related papers  (2022-03-01T09:43:02Z)
• proScript: Partially Ordered Scripts Generation via Pre-trained Language Models [49.03193243699244]
  We demonstrate for the first time that pre-trained neural language models (LMs) can be finetuned to generate high-quality scripts. We collected a large (6.4k), crowdsourced partially ordered scripts (named proScript) Our experiments show that our models perform well (e.g., F1=75.7 in task (i)), illustrating a new approach to overcoming previous barriers to script collection.
  arXiv  Detail & Related papers  (2021-04-16T17:35:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.