| Challenge: | Using computational literary analysis, we analyze novels, plays, and screenplays for their turning points. |
| Approach: | They propose to use turning points to analyze screenplays and plot synopses as tools for analysis . they propose to build a neural network model that identifies turning points in plot synoopse . |
| Outcome: | The proposed model outperforms baselines based on state-of-the-art sentence representations and expected position of turning points. |
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Screenplay Summarization Using Latent Narrative Structure (2020.acl-main)
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| Challenge: | Experimental results show that latent turning points improve summarization performance over general extractive summarizing models. |
| Approach: | They propose to explicitly incorporate the underlying structure of narratives into extractive summarization models by treating it as latent. |
| Outcome: | The proposed model improves on the CSI corpus of screenplays on a CSI episode . it shows that latent turning points correlate with important aspects of the document . |
What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis (N18-1)
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| Challenge: | Using movie overviews, we can gain a general impression of a movie by summarizing its content, genre, and artistic style. |
| Approach: | They propose a novel end-to-end model that generates movie overviews from an online database and a multi-label encoder for identifying screenplay attributes. |
| Outcome: | The proposed model reliably assigns good labels for movie attributes and generates sentences conditioned on the identified attributes. |
MovieSum: An Abstractive Summarization Dataset for Movie Screenplays (2024.findings-acl)
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| Challenge: | Movie screenplay summarization requires an understanding of long input contexts and elements unique to movies. |
| Approach: | They propose a dataset for movie screenplay summarization that includes movie screenplayers accompanied by their Wikipedia plot summaries. |
| Outcome: | The proposed dataset includes 2200 movie screenplays accompanied by their Wikipedia plot summaries. |
DiscoGraMS: Enhancing Movie Screen-Play Summarization using Movie Character-Aware Discourse Graph (2025.naacl-short)
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| Challenge: | Recent attempts at screenplay summarization focus on fine-tuning transformer-based pre-trained models, but these models often fall short in capturing long-term dependencies and latent relationships. |
| Approach: | They propose a novel resource that represents movie scripts as a movie character-aware discourse graph (CaD Graph) this resource aims to preserve all salient information, offering a more comprehensive and faithful representation of the screenplay’s content. |
| Outcome: | The proposed model preserves all salient information, offering a more comprehensive and faithful representation of the screenplay’s content. |
AligNarr: Aligning Narratives on Movies (2021.acl-short)
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| Challenge: | Experimental results show the viability of an unsupervised approach to align movie scripts with plot summaries. |
| Approach: | They propose an unsupervised method to align movie scripts with plot summaries using a global optimization model. |
| Outcome: | The proposed method outperforms a baseline alignment model on ten movies with 76% F1 score. |
Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues (2022.lrec-1)
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Sandhya Singh, Prapti Roy, Nihar Sahoo, Niteesh Mallela, Himanshu Gupta, Pushpak Bhattacharyya, Milind Savagaonkar, Nidhi Sultan, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta
| Challenge: | Movies reflect society and also hold power to transform opinions. |
| Approach: | They propose to annotate movie scripts for identity bias using a dataset that is annotated for gender, race/ethnicity, religion, age, occupation, LGBTQ, and other . |
| Outcome: | The proposed dataset contains dialogue turns annotated for gender, race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains biases like body shaming, personality bias, etc. |
Character Coreference Resolution in Movie Screenplays (2023.findings-acl)
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| Challenge: | Movie screenplays have a distinct narrative structure. |
| Approach: | They develop a method to extract structural information and character coreference clusters from movie screenplays by leveraging a movie parser and a character coreferser. |
| Outcome: | The proposed methods scale to long movie screenplays without dramatically increasing their memory footprints. |
Analyzing Film Adaptation through Narrative Alignment (2023.emnlp-main)
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| Challenge: | a new study examines the book-to-film adaptation process by examining the differences between the two media . novel adaptations often require dropping sections of the source text from the movie script . |
| Approach: | They use a Smith-Waterman local alignment algorithm to quantify text similarity between scenes and book units. |
| Outcome: | The proposed method reveals that novel adaptations often require dropping parts of the source text from the movie script. |
Select and Summarize: Scene Saliency for Movie Script Summarization (2024.findings-naacl)
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| Challenge: | Existing models for summarizing long-form narrative texts are computationally and memory limited. |
| Approach: | They propose a scene saliency dataset that consists of human-annotated salient scenes for 100 movies. |
| Outcome: | The proposed model outperforms state-of-the-art models and reflects the information content of a movie more accurately than a model that takes the whole movie script as input. |
MPST: A Corpus of Movie Plot Synopses with Tags (L18-1)
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| Challenge: | a corpus of movie plot synopses and tags can be used to build automatic tagging systems . a method to collect these tags allows us to learn to predict tags from plot synoopsis . |
| Approach: | They propose to collect a corpus of movie plot synopses and 70 tags to analyze their properties. |
| Outcome: | The proposed method can be used to predict movie tags from plot synopses. |