SummScreen: A Dataset for Abstractive Screenplay Summarization
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We introduce SUMMSCREEN, a summarization dataset comprised of pairs of TV series transcripts and human written recaps.The dataset provides a challenging testbed for abstractive summarization for several reasons.Plot details are often expressed indirectly in character dialogues and may be scattered across the entirety of the transcript.These details must be found and integrated to form the succinct plot descriptions in the recaps.Also, TV scripts contain content that does not directly pertain to the central plot but rather serves to develop characters or provide comic relief.This information is rarely contained in recaps.Since characters are fundamental to TV series, we also propose two entity-centric evaluation metrics.Empirically, we characterize the dataset by evaluating several methods, including neural models and those based on nearest neighbors.An oracle extractive approach outperforms all benchmarked models according to automatic metrics, showing that the neural models are unable to fully exploit the input transcripts.Human evaluation and qualitative analysis reveal that our nonoracle models are competitive with their oracle counterparts in terms of generating faithful plot events and can benefit from better content selectors.Both oracle and non-oracle models generate unfaithful facts, suggesting future research directions.
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Summ-Screen is an abstractive summarization dataset combining TV series transcripts and episode recaps, constructed from fan-contributed websites.
“The original dataset is an abstractive summarization dataset combining TV series transcripts and episode recaps, constructed from fan-contributed websites.”
From L-Eval: Instituting Standardized Evaluation for Long Context Language Models (2024) · cites this paper as Chen et al., 2022
Events in LOCOMO are densely annotated lists of life events that are hard to summarize due to temporal and causal coreferences present in the dialogues.
“The events in LOCOMO are densely annotated lists of life events that are hard to summarize due to temporal and causal coreferences present in the dialogues, in contrast to existing summarization benchmarks of research papers (Li et al., 2023a), movie scripts (Chen et al., 2022), books (Kry´ sci´ nski et al., 2022), emails (Zhang et al., 2021b) etc.”
From Evaluating Very Long-Term Conversational Memory of LLM Agents (2024) · cites this paper as Chen et al., 2022
SummScreen is an abstractive summarization dataset combining TV series transcripts and episode recaps, constructed from fan-contributed websites.
“This dataset originates from the SummScreen (Chen et al., 2022), the original dataset is an abstractive summarization dataset combining TV series transcripts and episode recaps.SummScreen is constructed from fan-contributed websites.”
From L-Eval: Instituting Standardized Evaluation for Long Context Language Models (2023) · cites this paper as Chen et al., 2022
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Garg, P. K., Chakraborty, R., & Dandapat, S. K. (2025) · ACM Transactions on the Web · doi:10.1145/3711908 · Cited by 6 · see citing works
End-to-End Long Document Summarization using Gradient Caching
Saxena, R., Tang, H., & Keller, F. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.40 · Cited by 5 · see citing works
Hierarchical3D Adapters for Long Video-to-text Summarization
Papalampidi, P., & Lapata, M. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.96 · Cited by 5 · see citing works
Multistage Mixed-Attention Unsupervised Keyword Extraction for Summary Generation
Wu, D., Cheng, P., & Zheng, Y. (2024) · Applied Sciences · doi:10.3390/app14062435 · Cited by 4 · see citing works
Zhao, M., Wang, L., Ji, H., Jiang, Z., Li, R., Lu, X., & Hu, Z. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110927 · Cited by 4 · see citing works
TVRecap: A Dataset for Generating Stories with Character Descriptions.Preprint
Chen, M., & Gimpel, K. (2021) · arXiv (Cornell University) · Cited by 4 · see citing works
Self-citationAn Exploratory Study on Long Dialogue Summarization: What Works and What's NextPreprint
Zhang, Y., Ni, A., Yu, T., Zhang, R., Zhu, C., Deb, B., Çelikyılmaz, A., Awadallah, A. H., & Radev, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04609 · Cited by 4 · see citing works
Dong, Z., Tang, T., Li, J., Zhao, W. X., & Wen, J.-R. (2023) · arXiv · doi:10.48550/arxiv.2309.13345 · Cited by 7 · see citing works
“vided into normal summarization (Cohan et al., 2018; Huang et al., 2021; Chen et al., 2022) and query-based summarization (Zhong et al.”
BackgroundMethodEvaluating Very Long-Term Conversational Memory of LLM Agents
Maharana, A., Lee, D.-H., Tulyakov, S., Bansal, M., Barbieri, F., & Fang, Y. (2024) · arXiv · doi:10.48550/arxiv.2402.17753 · Cited by 3 · see citing works
HVCMM: A Hybrid-View Abstractive Model of Automatic Summary Generation for Teaching
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StreamHover: Livestream Transcript Summarization and AnnotationPreprint
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Generating, retrieving persona and generating responses for long-term open-domain dialogue
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“Previously on…” from Recaps to Story Summarization
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Background Summarization of Event Timelines
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AURA: Automated Analysis and Reporting of Therapeutically Applied Table-top Role-Playing Games
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LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
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- Authors
- Chen, M., Chu, Z., Wiseman, S., & Gimpel, K.
- Affiliations
- Toyota Technological Institute at Chicago (US)
- University of Chicago (US)
- Duke University (US)
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 8602-8615
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3153603860
- Cited by
- 46
- References
- 56
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 16 · 2022: 4 · 2021: 9
- FWCI
- 4.42
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- abstractive summarization, screenplays, content selection, character speech, summarization datasets
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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