mlsanityData to 7 Oct 2026

NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization

arXiv 2212.01476 ↗First posted 2 Dec 2022cs.CLcited by 5

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Abstract (arXiv)

Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Summarizing a narrative is challenging as it requires an understanding of event causality and character behaviors. To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset. It contains 122K narrative documents, which are collected from plot descriptions of movies and TV episodes with diverse genres, and their corresponding abstractive summaries. Experiments show that there is a large performance gap between humans and the state-of-the-art summarization models on NarraSum. We hope that this dataset will promote future research in summarization, as well as broader studies of natural language understanding and generation. The dataset is available at https://github.com/zhaochaocs/narrasum.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 39 of its 69 references that are in the map. First posted on arXiv 2 Dec 2022.

Builds on

39 of 69 references.

References · 39

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27 more
  1. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
  2. 2021QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationcited by 363
  3. 2015MovieQA: Understanding Stories in Movies through Question-Answeringcited by 304
  4. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 292
  5. 2021BookSum: A Collection of Datasets for Long-form Narrative Summarizationcited by 184
  6. 2017Controllable Abstractive Summarizationcited by 110
  7. 2015The Goldilocks Principle: Reading Children's Books with Explicit Memory Representationscited by 108
  8. 2018Abstractive Summarization of Reddit Posts with Multi-level Memory Networkscited by 102
  9. 2020Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articlescited by 96
  10. 2020Heterogeneous Graph Neural Networks for Extractive Document Summarizationcited by 90
  11. 2021SummScreen: A Dataset for Abstractive Screenplay Summarizationcited by 80
  12. 2021DocNLI: A Large-scale Dataset for Document-level Natural Language Inferencecited by 75
  13. 2018DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehensioncited by 70
  14. 2018Content Selection in Deep Learning Models of Summarizationcited by 64
  15. 2021"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understandingcited by 48
  16. 2021Can NLI Models Verify QA Systems' Predictions?cited by 46
  17. 2019Searching for Effective Neural Extractive Summarization: What Works and What's Nextcited by 38
  18. 2019Story Realization: Expanding Plot Events into Sentencescited by 35
  19. 2019Movie Plot Analysis via Turning Point Identificationcited by 33
  20. 2019A Graph-Based Framework to Bridge Movies and Synopsescited by 31
  21. 2020Exploring Content Selection in Summarization of Novel Chapterscited by 29
  22. 2021EmailSum: Abstractive Email Thread Summarizationcited by 24
  23. 2020Screenplay Summarization Using Latent Narrative Structurecited by 22
  24. 2021Salience-Aware Event Chain Modeling for Narrative Understandingcited by 14
  25. 2022Read Top News First: A Document Reordering Approach for Multi-Document News Summarizationcited by 9
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  27. 2022Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehensioncited by 3

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2025NarraBench: A Comprehensive Framework for Narrative Benchmarkingcited by 13
  2. 2025NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarizationcited by 10
  3. 2024MovieSum: An Abstractive Summarization Dataset for Movie Screenplayscited by 8
  4. 2025Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarizationcited by 6
  5. 2024T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Taskcited by 1