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Controlled Attribute-Specific Summarization of Interrogative Dialogues
Effective summarization of interrogative dialogues is a critical task in forensic and investigative settings, requiring high factual accuracy, coherence, and attribute-specific relevance. In this work, we introduce CASPER, a novel Chain-of-Thought Attribute-Specific Prompting for Evaluative Summarization framework that leverages structured prompting and iterative refinement to generate high-quality summaries of interrogator-witness interactions. We construct MINDSum, a dataset extending the MIND corpus, comprising 6,000 utterance pairs annotated with event details, factual statements, characte
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-23T12:32:28.000Z
First collected: 2026-09-24T01:22:21.678Z. This is not the publication date.