Multi-view Story Characterization from Movie Plot Synopses and Reviews (2020.emnlp-main)
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| Challenge: | Existing methods for characterizing stories by generating tags from synopses suffer from coverage issues. |
| Approach: | They propose to use synopses and reviews to characterize stories by inferring attributes such as theme and style from written synopsis and reviews. |
| Outcome: | The proposed model improves over methods that only use synopses and reviews . it can extract a complementary set of story attributes from reviews without supervision . |
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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 . |
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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. |
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| Challenge: | Existing systems that generate tags for movies can help users better retrieve movies based on their personal preferences and user profiles. |
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MBTI Personality Prediction for Fictional Characters Using Movie Scripts (2022.findings-emnlp)
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| Challenge: | Graph databases are well-suited for crossreferencing information from multiple sources to support machine learning tasks. |
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StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning (2022.emnlp-main)
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| Challenge: | Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference. |
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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. |
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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. |