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Long-Form Narrative Summarization

Text Summarization › Long-Form Narrative Summarization 30 평가 기록 4 datasets

Summarizing long-form narratives, such as books, movies, and TV scripts, remains an open challenge in NLP. Unlike news or document summarization, narratives require capturing intricate plotlines, evolving character relationships, and thematic coherence over tens of thousands of tokens. The hybrid structure of narratives, which combines descriptive prose with multi-speaker dialogues, implicit inference, and dynamic topic shifts, adds further complexity, demanding an approach that preserves contextual integrity while condensing information effectively. Furthermore, the sheer length of narrative texts, typically ranging from 40K to 160K tokens, poses significant challenges for standard summarization models.

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Dataset기록된 모델 예시MetricPaperCode
NexusSum (Mistral Large)
BERTScore (F1): 70.70ROUGE (geometric mean of 1/2/L): 18.27ROUGE-1: 42.51ROUGE-2: 10.27
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NexusSum (Mistral Large)
BERTScore (F1): 65.73ROUGE-1: 44.91ROUGE-2: 11.43ROUGE-L: 19.23
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NexusSum (Mistral Large)
BERTScore (F1): 63.53ROUGE-1: 44.91ROUGE-2: 11.43ROUGE-L: 19.23
논문 -
NexusSum (Mistral Large)
BERTScore (F1): 61.59ROUGE-1: 30.44ROUGE-2: 6.40ROUGE-L: 17.95
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