为财报电话会设计多问题生成框架，自动提炼分析师可能提问。
Co-Trained Retriever-Generator Framework for Question Generation in Earnings Calls
- 用检索增强策略从财报文本中提取关键信息生成问题
- 在真实财报语料上验证，生成问题准确率与一致性显著提升
- 适合金融、NLP交叉研究者及智能投研系统开发者
在学术会议到企业财报电话会等专业场景中，预判听众问题能力至关重要。传统依赖人工评估听众背景的方法在大规模或异质群体中效率低下。尽管自然语言处理在文本问答生成方面取得进展，但主要聚焦于学术场景，对财报电话会等专业领域关注不足。本文首次提出专为财报电话会设计的多问题生成（MQG）任务，通过收集大量财报通话记录并引入新型标注方法分类潜在问题，结合检索增强策略提取相关信息。目标是直接从财报内容生成分析师可能提出的多样化问题。实证评估显示，该方法在生成问题的准确性、一致性和困惑度方面表现优异。
原文摘要 · Abstract (English)
In diverse professional environments, ranging from academic conferences to corporate earnings calls, the ability to anticipate audience questions stands paramount. Traditional methods, which rely on manual assessment of an audience's background, interests, and subject knowledge, often fall short - particularly when facing large or heterogeneous groups, leading to imprecision and inefficiency. While NLP has made strides in text-based question generation, its primary focus remains on academic settings, leaving the intricate challenges of professional domains, especially earnings call conferences, underserved. Addressing this gap, our paper pioneers the multi-question generation (MQG) task specifically designed for earnings call contexts. Our methodology involves an exhaustive collection of earnings call transcripts and a novel annotation technique to classify potential questions. Furthermore, we introduce a retriever-enhanced strategy to extract relevant information. With a core aim of generating a spectrum of potential questions that analysts might pose, we derive these directly from earnings call content. Empirical evaluations underscore our approach's edge, revealing notable excellence in the accuracy, consistency, and perplexity of the questions generated.
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