受控的审讯对话属性特定摘要生成

中文摘要 AI 辅助

在法医和调查场景中，对审讯对话进行有效摘要是一项关键任务，需要高事实准确性、连贯性和属性特定相关性。在这项工作中，我们引入了CASPER，一种新颖的基于思维链的属性特定提示评估摘要框架，该框架利用结构化提示和迭代细化来生成审讯者与证人互动的高质量摘要。我们构建了MINDSum数据集，该数据集扩展了MIND语料库，包含6,000个话语对，并标注了事件细节、事实陈述、角色描述和填充词。CASPER采用RoleEval，一种分层评估机制，其中多个角色（警官、检查员、高级检查员）根据预定义标准迭代评估摘要。通过整合实体提取和结构化反馈循环，与现有基线相比，CASPER显著提高了事实一致性和上下文完整性。实验结果表明，我们的框架在词汇（ROUGE）和语义（BERTScore）指标上均优于标准摘要模型，而人工评估确认了其与专家推理的一致性。我们的发现强调了在高风险领域中进行受控摘要的潜力，为AI驱动的法医情报铺平了道路。

英文摘要

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, character descriptions, and fillers. CASPER employs RoleEval, a hierarchical evaluation mechanism where multiple roles (officer, inspector, senior inspector) iteratively assess summaries based on predefined criteria. By integrating entity extraction and structured feedback loops, CASPER significantly improves factual consistency and contextual completeness compared to existing baselines. Experimental results demonstrate that our framework outperforms standard summarization models on both lexical (ROUGE) and semantic (BERTScore) metrics, while human evaluation confirms its alignment with expert reasoning. Our findings underscore the potential of controlled summarization in high-stakes domains, paving the way for AI-driven forensic intelligence.

发表机构

• IIIT Delhi（德里印度理工学院）

机构由 AI 辅助整理，请以论文原文为准。