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Investigating the Impact of Text Summarization on Topic Modeling

arXiv 2410.09063 ↗First posted 28 Sep 2024cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 28 Sep 2024.

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References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  6. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  7. 2020Top2Vec: Distributed Representations of Topicscited by 124
  8. 2020Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherencecited by 121
  9. 2024Large Language Models Offer an Alternative to the Traditional Approach of Topic Modellingcited by 35
  10. 2023Prompting Large Language Models for Topic Modelingcited by 24
  11. 2023vONTSS: vMF based semi-supervised neural topic modeling with optimal transportcited by 16

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