Analyzing Film Adaptation through Narrative Alignment
- URL: http://arxiv.org/abs/2311.04020v1
- Date: Tue, 7 Nov 2023 14:18:03 GMT
- Title: Analyzing Film Adaptation through Narrative Alignment
- Authors: Tanzir Pial, Shahreen Salim, Charuta Pethe, Allen Kim, Steven Skiena
- Abstract summary: Novels are often adapted into feature films, but differences between the two media usually require dropping sections of the source text from the movie script.
Here we study this screen adaptation process by constructing narrative alignments using the Smith-Waterman local alignment algorithm and SBERT embedding distance to quantify text similarity between scenes and book units.
We use these alignments to perform an automated analysis of 40 adaptations, revealing insights into the screenwriting process concerning (i) faithfulness of adaptation, (ii) importance of dialog, (iii) of narrative order, and (iv) gender representation issues reflective of the Bechdel test.
- Score: 11.304581370821756
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Novels are often adapted into feature films, but the differences between the
two media usually require dropping sections of the source text from the movie
script. Here we study this screen adaptation process by constructing narrative
alignments using the Smith-Waterman local alignment algorithm coupled with
SBERT embedding distance to quantify text similarity between scenes and book
units. We use these alignments to perform an automated analysis of 40
adaptations, revealing insights into the screenwriting process concerning (i)
faithfulness of adaptation, (ii) importance of dialog, (iii) preservation of
narrative order, and (iv) gender representation issues reflective of the
Bechdel test.
Related papers
- Contextual Speech Extraction: Leveraging Textual History as an Implicit Cue for Target Speech Extraction [50.630431647192054]
This paper investigates a novel approach for Target Speech Extraction (TSE)
It relies solely on textual context to extract the target speech.
We present three CSE models and analyze their performances on three datasets.
arXiv Detail & Related papers (2025-03-11T18:26:10Z) - Scene Graph Generation with Role-Playing Large Language Models [50.252588437973245]
Current approaches for open-vocabulary scene graph generation (OVSGG) use vision-language models such as CLIP.
We propose SDSGG, a scene-specific description based OVSGG framework.
To capture the complicated interplay between subjects and objects, we propose a new lightweight module called mutual visual adapter.
arXiv Detail & Related papers (2024-10-20T11:40:31Z) - DiscoGraMS: Enhancing Movie Screen-Play Summarization using Movie Character-Aware Discourse Graph [6.980991481207376]
We introduce DiscoGraMS, a novel resource that represents movie scripts as a movie character-aware discourse graph (CaD Graph)
The model aims to preserve all salient information, offering a more comprehensive and faithful representation of the screenplay's content.
arXiv Detail & Related papers (2024-10-18T17:56:11Z) - 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) - Interconnected Kingdoms: Comparing 'A Song of Ice and Fire' Adaptations Across Media Using Complex Networks [2.653724344357519]
We propose several methods to match characters between media and compare their position in the networks.
We apply these methods to the novel series textitA Song of Ice and Fire, by G.R.R. Martin, and its comics and TV show adaptations.
arXiv Detail & Related papers (2024-10-07T19:35:46Z) - Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment [53.12952107996463]
This work proposes a novel training framework for learning to localize temporal boundaries of procedure steps in training videos.
Motivated by the strong capabilities of Large Language Models (LLMs) in procedure understanding and text summarization, we first apply an LLM to filter out task-irrelevant information and summarize task-related procedure steps from narrations.
To further generate reliable pseudo-matching between the LLM-steps and the video for training, we propose the Multi-Pathway Text-Video Alignment (MPTVA) strategy.
arXiv Detail & Related papers (2024-09-22T18:40:55Z) - 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) - Contextualized Diffusion Models for Text-Guided Image and Video Generation [67.69171154637172]
Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing.
We propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample.
We generalize our model to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing.
arXiv Detail & Related papers (2024-02-26T15:01:16Z) - GNAT: A General Narrative Alignment Tool [12.100007440638667]
We develop a general approach to narrative alignment coupling the Smith-Waterman algorithm from bioinformatics with modern text similarity metrics.
We apply and evaluate our general narrative alignment tool (GNAT) on four distinct problem domains differing greatly in both the relative and absolute length of documents.
arXiv Detail & Related papers (2023-11-07T00:24:14Z) - Temporal Perceiving Video-Language Pre-training [112.1790287726804]
This work introduces a novel text-video localization pre-text task to enable fine-grained temporal and semantic alignment.
Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description.
Our method connects the fine-grained frame representations with the word representations and implicitly distinguishes representations of different instances in the single modality.
arXiv Detail & Related papers (2023-01-18T12:15:47Z)
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.