| Challenge: | Existing models for analyzing salience of discourse units are inadequate . authors propose two saliency detection models based on discourse relations . |
| Approach: | They propose two salience detection models based on discourse relations that capture complex interactions between discourse units. |
| Outcome: | The proposed models outperform the strong frequency baseline and improve the feature based model by a large margin. |
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| Challenge: | Existing methods for estimating event salience without annotations are prohibitively costly because they require annotators to understand the concept of event salientity. |
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Rajarshi Bhowmik, Marco Ponza, Atharva Tendle, Anant Gupta, Rebecca Jiang, Xingyu Lu, Qian Zhao, Daniel Preotiuc-Pietro
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| Challenge: | Existing models for detecting events as subevents have been developed for analyzing textual understanding. |
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| Challenge: | Storytelling is the communication of interesting and related events that form a concrete process. |
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| Challenge: | Existing methods for predicting implicit discourse relations ignore wider paragraph contexts beyond the two discourse units examined for a discourse relation prediction. |
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| Challenge: | Detecting salient events is an essential part of understanding narrative, and is used to aid storyline writing and summarisation. |
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Contrastive Learning with Narrative Twins for Modeling Story Salience (2026.eacl-long)
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| Challenge: | Understanding narratives requires identifying which events are most salient for a story’s progression. |
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