用大模型摘要文档再做主题建模，能提升主题多样性。
Investigating the Impact of Text Summarization on Topic Modeling
- 先用大模型生成文档摘要，再输入主题模型
- 不同摘要长度影响主题效果，存在最优长度
- 尤其适合长文档，提升主题多样性
主题模型用于识别和归类一组文档中的相似主题。基于深度学习的神经主题模型近年来受到广泛关注。本文提出一种新方法：在输入主题模型前，利用预训练大语言模型（LLM）对文档生成摘要。采用少样本提示（few-shot prompting）生成不同长度的摘要，以比较其对主题建模的影响。该方法在处理较长文档时尤为有效，能捕捉核心信息，减少噪声与无关细节对整体主题的干扰。实验发现，不同数据集存在最优摘要长度，能显著提升主题建模性能。相比先前模型，本方法在主题多样性上表现更优，同时保持相当的连贯性得分。
原文摘要 · Abstract (English)
Topic models are used to identify and group similar themes in a set of documents. Recent advancements in deep learning based neural topic models has received significant research interest. In this paper, an approach is proposed that further enhances topic modeling performance by utilizing a pre-trained large language model (LLM) to generate summaries of documents before inputting them into the topic model. Few shot prompting is used to generate summaries of different lengths to compare their impact on topic modeling. This approach is particularly effective for larger documents because it helps capture the most essential information while reducing noise and irrelevant details that could obscure the overall theme. Additionally, it is observed that datasets exhibit an optimal summary length that leads to improved topic modeling performance. The proposed method yields better topic diversity and comparable coherence values compared to previous models.
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