Investigating the Impact of Text Summarization on Topic Modeling

TL;DR

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 to yield better topic diversity and comparable coherence values compared to previous models.

Abstract

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.

Figures (2)

• Figure 1: This diagram outlines three experimental cases: two where LLM-generated summaries are used as inputs for the topic model, and one where the original data is used directly for topic modeling.
• Figure 2: Topic modeling performance for various hyperparameter combinations.