# Movie Plot Analysis via Turning Point Identification

arXiv: https://arxiv.org/abs/1908.10328 · HTML: https://mlsanity.com/paper/1908.10328/

## Key facts

- Cited by 33 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 30 of 33)

- [Are Large Language Models Capable of Generating Human-Level Narratives?](https://mlsanity.com/paper/2407.13248/) (2024)
- [AutoAD: Movie Description in Context](https://mlsanity.com/paper/2303.16899/) (2023)
- [From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding](https://mlsanity.com/paper/2409.18938/) (2024)
- [Personality Understanding of Fictional Characters during Book Reading](https://mlsanity.com/paper/2305.10156/) (2023)
- [Finding the Right Moment: Human-Assisted Trailer Creation via Task Composition](https://mlsanity.com/paper/2111.08774/) (2021)
- [Hierarchical3D Adapters for Long Video-to-text Summarization](https://mlsanity.com/paper/2210.04829/) (2022)
- [A Survey of Machine Narrative Reading Comprehension Assessments](https://mlsanity.com/paper/2205.00299/) (2022)
- [Select and Summarize: Scene Saliency for Movie Script Summarization](https://mlsanity.com/paper/2404.03561/) (2024)
- [[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh My! Literary Coreference Annotation with LLMs](https://mlsanity.com/paper/2401.17922/) (2024)
- [Long Story Short: a Summarize-then-Search Method for Long Video Question Answering](https://mlsanity.com/paper/2311.01233/) (2023)
- [Knowledge Enhanced Model for Live Video Comment Generation](https://mlsanity.com/paper/2304.14657/) (2023)
- [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://mlsanity.com/paper/2212.01476/) (2022)
- [ScreenWriter: Automatic Screenplay Generation and Movie Summarisation](https://mlsanity.com/paper/2410.19809/) (2024)
- [Detecting Narrative Elements in Informational Text](https://mlsanity.com/paper/2210.03028/) (2022)
- [MEGA: Multimodal Alignment Aggregation and Distillation For Cinematic Video Segmentation](https://mlsanity.com/paper/2308.11185/) (2023)
- [Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations](https://mlsanity.com/paper/2506.03941/) (2025)
- [Movie Facts and Fibs (MF2): A Benchmark for Long Movie Understanding](https://mlsanity.com/paper/2506.06275/) (2025)
- [Narrix: Remixing Narrative Strategies from Examples for Story Writing](https://mlsanity.com/paper/2604.07643/) (2026)
- [THEaiTRE 1.0: Interactive generation of theatre play scripts](https://mlsanity.com/paper/2102.08892/) (2021)
- [M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations](https://mlsanity.com/paper/2302.09418/) (2023)
- [Previously on the Stories: Recap Snippet Identification for Story Reading](https://mlsanity.com/paper/2402.07271/) (2024)
- [BookWorm: A Dataset for Character Description and Analysis](https://mlsanity.com/paper/2410.10372/) (2024)
- [Actionable Entities Recognition Benchmark for Interactive Fiction](https://mlsanity.com/paper/2109.13855/) (2021)
- [Contrastive Learning with Narrative Twins for Modeling Story Salience](https://mlsanity.com/paper/2601.07765/) (2026)
- [Branching Narratives: Character Decision Points Detection](https://mlsanity.com/paper/2405.07282/) (2024)
- ["Previously on ..." From Recaps to Story Summarization](https://mlsanity.com/paper/2405.11487/) (2024)
- [A Decomposition-Based Approach for Evaluating and Analyzing Inter-Annotator Disagreement](https://mlsanity.com/paper/2206.05446/) (2022)
- [Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learning](https://mlsanity.com/paper/2402.06560/) (2024)
- [MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations](https://mlsanity.com/paper/2409.14801/) (2024)
- [LitSeg: Narrative-Aware Document Segmentation for Literary RAG](https://mlsanity.com/paper/2605.27156/) (2026)

## Abstract (arXiv)

According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e.g., setup, complications, aftermath). We propose the task of turning point identification in movies as a means of analyzing their narrative structure. We argue that turning points and the segmentation they provide can facilitate processing long, complex narratives, such as screenplays, for summarization and question answering. We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays. Our model outperforms strong baselines based on state-of-the-art sentence representations and the expected position of turning points.