S^2tory uses narratological theory and a Narrative Expert Agent to identify plot nuclei in movie scripts for high-fidelity summarization at 3.5x compression, with strong zero-shot generalization to books.
In: Proceedings of the 2004 conference on empirical methods in natural language processing
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MSEA uses a master-slave encoder architecture on patent specifications and claims, enhanced with pointer networks and repetition suppression, to generate better summaries as measured by small ROUGE score gains.
citing papers explorer
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S^2tory: Story Spine Distillation for Movie Script Summarization
S^2tory uses narratological theory and a Narrative Expert Agent to identify plot nuclei in movie scripts for high-fidelity summarization at 3.5x compression, with strong zero-shot generalization to books.
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The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims
MSEA uses a master-slave encoder architecture on patent specifications and claims, enhanced with pointer networks and repetition suppression, to generate better summaries as measured by small ROUGE score gains.