MPST: A Corpus of Movie Plot Synopses with Tags
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Abstract (arXiv)
Social tagging of movies reveals a wide range of heterogeneous information about movies, like the genre, plot structure, soundtracks, metadata, visual and emotional experiences. Such information can be valuable in building automatic systems to create tags for movies. Automatic tagging systems can help recommendation engines to improve the retrieval of similar movies as well as help viewers to know what to expect from a movie in advance. In this paper, we set out to the task of collecting a corpus of movie plot synopses and tags. We describe a methodology that enabled us to build a fine-grained set of around 70 tags exposing heterogeneous characteristics of movie plots and the multi-label associations of these tags with some 14K movie plot synopses. We investigate how these tags correlate with movies and the flow of emotions throughout different types of movies. Finally, we use this corpus to explore the feasibility of inferring tags from plot synopses. We expect the corpus will be useful in other tasks where analysis of narratives is relevant.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Feb 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 4; 2025: 1; 2026: 1.
11 papers.
- 2024Leveraging Large Language Models for NLG Evaluation: Advances and Challengescited by 37
- 2023Learning Personalized Alignment for Evaluating Open-ended Text Generationcited by 29
- 2024Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMscited by 17
- 2024Exploration of Masked and Causal Language Modelling for Text Generationcited by 7
- 2021StoryDB: Broad Multi-language Narrative Datasetcited by 6
- 2022What is Wrong with Language Models that Can Not Tell a Story?cited by 4
- 2021TrUMAn: Trope Understanding in Movies and Animationscited by 3
- 2021Situation and Behavior Understanding by Trope Detection on Filmscited by 2
- 2024Branching Narratives: Character Decision Points Detectioncited by 1
- 2025PREFINE: Personalized Story Generation via Simulated User Critics and User-Specific Rubric Generationcited by 1
- 2026Can LLMs Imagine Moral Alternatives Beyond Binary Dilemmas?cited by 0
- 2026Can LLMs Imagine Moral Alternatives Beyond Binary Dilemmas?30 Jun 2026
- 2025PREFINE: Personalized Story Generation via Simulated User Critics and User-Specific Rubric Generation16 Sep 2025
- 2024Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs17 Sep 2024
- 2024Exploration of Masked and Causal Language Modelling for Text Generation21 May 2024
- 2024Branching Narratives: Character Decision Points Detection12 May 2024
- 2024Leveraging Large Language Models for NLG Evaluation: Advances and Challenges13 Jan 2024
- 2023Learning Personalized Alignment for Evaluating Open-ended Text Generation5 Oct 2023
- 2022What is Wrong with Language Models that Can Not Tell a Story?9 Nov 2022
- 2021StoryDB: Broad Multi-language Narrative Dataset29 Sep 2021
- 2021TrUMAn: Trope Understanding in Movies and Animations10 Aug 2021
- 2021Situation and Behavior Understanding by Trope Detection on Films19 Jan 2021