| 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. |
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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. |
GNAT: A General Narrative Alignment Tool (2023.emnlp-main)
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| Challenge: | Algorithmic sequence alignment is a common operation in many NLP tasks, but it is difficult to recognize similarities between distant versions of narratives such as translations and retellings. |
| Approach: | They propose a general approach to narrative alignment coupling the Smith-Waterman algorithm from bioinformatics with modern text similarity metrics. |
| Outcome: | The proposed approach can be used to identify similarities between two different versions of narratives, and to define rigorous p-values on the significance of any alignment. |
Movie Plot Analysis via Turning Point Identification (D19-1)
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| 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. |
Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding (2024.findings-emnlp)
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| Challenge: | Story video-text alignment is a core task in computational story understanding, but its progress has been held back by the scarcity of manually annotated video- text correspondences and the heavy concentration on English narrations of Hollywood movies. |
| Approach: | They construct a multilingual video story dataset with 13,166 movie summary videos from 7 languages and manual annotations of fine-grained video-text correspondences. |
| Outcome: | The proposed approach outperforms the SOTA methods on clip accuracy and Sentence IoU scores. |
Story Embeddings — Narrative-Focused Representations of Fictional Stories (2024.emnlp-main)
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| Challenge: | Existing approaches to model fictional narratives have focused on the aspect of "what" rather than "how" they are being told. |
| Approach: | They propose a model that embeds stories such that similar stories will result in similar embeddings. |
| Outcome: | The proposed model shows state-of-the-art performance on multiple retrieval tasks and a narrative understanding task. |
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. |
Annotation and Evaluation of Coreference Resolution in Screenplays (2021.findings-acl)
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| Challenge: | Screenplays refer to characters using different names, pronouns, and nominal expressions. |
| Approach: | They develop an automatic screenplay parser to extract structural information and design coreference rules based upon the structure of screenplays. |
| Outcome: | The proposed model outperforms a benchmark model on the screenplay coreference resolution task. |
Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes (N18-2)
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| Challenge: | Existing methods to identify instances of similar narratives are limited by annotated data. |
| Approach: | They propose a task for identifying instances of similar narratives from a collection of narrative texts. |
| Outcome: | The proposed approach yields an 8% absolute improvement over a baseline on a novel dataset of plot summaries of 577 movie remakes from Wikipedia. |
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. |
Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)
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| Challenge: | We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries. |
| Approach: | They propose a new metric for aligning summary sentences with chapter sentences to create gold extracts. |
| Outcome: | The proposed method improves on previous methods and automatic metrics and a crowd-sourced pyramid analysis. |