CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing
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Abstract (arXiv)
This paper introduces the shared task of summarizing documents in several creative domains, namely literary texts, movie scripts, and television scripts. Summarizing these creative documents requires making complex literary interpretations, as well as understanding non-trivial temporal dependencies in texts containing varied styles of plot development and narrative structure. This poses unique challenges and is yet underexplored for text summarization systems. In this shared task, we introduce four sub-tasks and their corresponding datasets, focusing on summarizing books, movie scripts, primetime television scripts, and daytime soap opera scripts. We detail the process of curating these datasets for the task, as well as the metrics used for the evaluation of the submissions. As part of the CREATIVESUMM workshop at COLING 2022, the shared task attracted 18 submissions in total. We discuss the submissions and the baselines for each sub-task in this paper, along with directions for facilitating future work in the field.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 10 Nov 2022.
Builds on
13 of 27 references.
References · 13
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2020Longformer: The Long-Document Transformercited by 3,413
- 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 407
- 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 294
- 2021Recursively Summarizing Books with Human Feedbackcited by 290
- 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 225
- 2021BookSum: A Collection of Datasets for Long-form Narrative Summarizationcited by 184
- 2022SCROLLS: Standardized CompaRison Over Long Language Sequencescited by 170
- 2021SummScreen: A Dataset for Abstractive Screenplay Summarizationcited by 79
- 2021Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarizationcited by 51
- 2021Finding a Balanced Degree of Automation for Summary Evaluationcited by 43
- 2022Long Document Summarization with Top-down and Bottom-up Inferencecited by 42
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Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 1.
5 papers.
- 2022Socratic Pretraining: Question-Driven Pretraining for Controllable Summarizationcited by 17
- 2024A Modular Approach for Multimodal Summarization of TV Showscited by 13
- 2024Select and Summarize: Scene Saliency for Movie Script Summarizationcited by 10
- 2024ScreenWriter: Automatic Screenplay Generation and Movie Summarisationcited by 5
- 2025Fine-Tuned LLMs are "Time Capsules" for Tracking Societal Bias Through Bookscited by 5
- 2025Fine-Tuned LLMs are "Time Capsules" for Tracking Societal Bias Through Books7 Feb 2025
- 2024ScreenWriter: Automatic Screenplay Generation and Movie Summarisation17 Oct 2024
- 2024Select and Summarize: Scene Saliency for Movie Script Summarization4 Apr 2024
- 2024A Modular Approach for Multimodal Summarization of TV Shows6 Mar 2024
- 2022Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization20 Dec 2022