| Challenge: | Existing summarization datasets are constructed from various domains, such as news, and we characterize them using two entity-centric metrics. |
| Approach: | They propose to use a summarization dataset to evaluate TV series transcripts and recaps . they propose to employ two entity-centric metrics to evaluate the dataset . |
| Outcome: | The proposed model outperforms the existing model and its oracle counterparts in character overlap and accuracy. |
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| Challenge: | Movie screenplay summarization requires an understanding of long input contexts and elements unique to movies. |
| Approach: | They propose a dataset for movie screenplay summarization that includes movie screenplayers accompanied by their Wikipedia plot summaries. |
| Outcome: | The proposed dataset includes 2200 movie screenplays accompanied by their Wikipedia plot summaries. |
A Modular Approach for Multimodal Summarization of TV Shows (2024.acl-long)
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| Challenge: | In this paper, we address the task of summarizing television shows, which touches key areas in AI research. |
| Approach: | They propose a modular approach where separate components perform specialized sub-tasks . they propose atomic facts to measure precision and recall of generated summaries . |
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SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization (2021.acl-demo)
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| Challenge: | despite advances in abstractive text summarization, the true performance and failure modes of modern neural models are not yet fully understood due to the black-box nature of neural models and unmanageable scale of recent datasets for manual analysis. |
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NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization (2022.findings-emnlp)
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| Challenge: | Existing studies focus on summarizing news documents or structured documents. |
| Approach: | They propose to use a large-scale narrative summarization dataset to encourage research . they find there is a performance gap between humans and the models on NarraSum . |
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SumTitles: a Summarization Dataset with Low Extractiveness (2020.coling-main)
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| Challenge: | Existing methods for extractive summarization of dialogue data are limited by the grammar and structure of the utterances used. |
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DiscoGraMS: Enhancing Movie Screen-Play Summarization using Movie Character-Aware Discourse Graph (2025.naacl-short)
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| Challenge: | Recent attempts at screenplay summarization focus on fine-tuning transformer-based pre-trained models, but these models often fall short in capturing long-term dependencies and latent relationships. |
| Approach: | They propose a novel resource that represents movie scripts as a movie character-aware discourse graph (CaD Graph) this resource aims to preserve all salient information, offering a more comprehensive and faithful representation of the screenplay’s content. |
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Abstractive Summarizers are Excellent Extractive Summarizers (2023.acl-short)
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| Challenge: | Abstractive summarization systems have traditionally been fragmented, limiting the benefits of compatible models. |
| Approach: | They propose three new inference algorithms using sequence-to-sequence architectures to model extractive summarization with an abstractive summmarization system. |
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Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples (N19-1)
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Screenplay Summarization Using Latent Narrative Structure (2020.acl-main)
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| Challenge: | Experimental results show that latent turning points improve summarization performance over general extractive summarizing models. |
| Approach: | They propose to explicitly incorporate the underlying structure of narratives into extractive summarization models by treating it as latent. |
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On the Abstractiveness of Neural Document Summarization (D18-1)
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| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
| Approach: | They propose to use a pure copy system to verify abstractiveness of document summarization systems. |
| Outcome: | The proposed system produces abstractive summaries while being far more efficient. |