探究文本摘要对主题建模的影响
Investigating the Impact of Text Summarization on Topic Modeling
AI总结：
本文提出利用预训练大型语言模型在输入主题模型前生成文档摘要以提升主题建模性能，发现该方法对长文档尤为有效且存在最优摘要长度，相比以往模型取得了更好的主题多样性与可比的连贯性。
摘要：
主题模型用于识别和分组一组文档中的相似主题。基于深度学习的神经主题模型的最新进展引起了广泛的研究兴趣。本文提出了一种方法，通过利用预训练大型语言模型（LLM）在将文档输入主题模型之前生成文档摘要，从而进一步提升主题建模性能。研究采用少样本提示生成不同长度的摘要，以比较它们对主题建模的影响。该方法对较长文档尤为有效，因为它有助于捕捉最关键的信息，同时减少可能掩盖整体主题的噪声和无关细节。此外，观察发现数据集存在一个最优摘要长度，能够带来更好的主题建模性能。与以往模型相比，所提出的方法产生了更好的主题多样性和可比的连贯性指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
