Papers with story quality

5 papers
Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation (N19-4)

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Challenge: a neural narrative generation system interacts with humans to generate stories . a recent resurgence of interest in collaborative storytelling has led to new approaches .
Approach: They propose a neural narrative generation system that interacts with humans to generate stories.
Outcome: The proposed system improves story quality and user engagement under time constraints.
Stretch-VST: Getting Flexible With Visual Stories (2021.acl-demo)

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Challenge: Existing visual storytelling models produce stories with fixed lengths of five sentences and the fix-length stories carry limited details and provide ambiguous textual information to the readers.
Approach: They propose to “stretch” visual storytelling frameworks by adding appropriate knowledge to the model to generate long stories.
Outcome: The proposed framework provides better focus and detail when long stories are generated without deteriorating the quality.
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling (P18-1)

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Challenge: Visual captioning is aimed at depicting the concrete content of images, but its capability of performing human-like understanding is still restrictive.
Approach: They propose an Adversarial REward Learning framework to learn an implicit reward function from human demonstrations and optimize policy search with the learned reward function.
Outcome: The proposed framework improves performance over state-of-the-art (SOTA) methods in cloning expert behaviors, but human evaluation shows that it achieves significant improvement in generating more human-like stories than SOTA systems.
Modeling Protagonist Emotions for Emotion-Aware Storytelling (2020.emnlp-main)

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Challenge: Cognitive scientists have pinpointed the central role of emotions in storytelling.
Approach: They propose to use Emotion Supervision and two Emotion-Reinforced models to generate stories that follow the desired emotion arcs for the protagonist.
Outcome: The proposed models generate stories that follow the desired emotion arcs without sacrificing story quality.
Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and Repetition (2024.findings-emnlp)

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Challenge: Visual storytelling is a task of generating a story for a sequence of several temporally-ordered images or video frames.
Approach: They propose a method that measures story quality in terms of human likeness regarding three key aspects highlighted in previous work: visual grounding, coherence, and repetitiveness.
Outcome: The proposed method improves on the foundation model LLaVA but only slightly compared to TAPM, a 50-times smaller visual storytelling model.

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