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A Study of Unsupervised Summarization Using Topic Model

  • Journal of Software Assessment and Valuation
  • Abbr : JSAV
  • 2023, 19(1), pp.77-88
  • DOI : 10.29056/jsav.2023.3.10
  • Publisher : Korea Software Assessment and Valuation Society
  • Research Area : Engineering > Computer Science
  • Received : March 15, 2023
  • Accepted : March 20, 2023
  • Published : March 31, 2023

Hyunjin Bae 1 Chulyun Kim 1

1숙명여자대학교

Accredited

ABSTRACT

Automatic Document Summarization is a research field that aims to create short summaries of documents while maintaining their important content. So far, document summarization has relied on supervised deep neural network models trained on large datasets. However, despite increasing demand from industry, there is still a shortage of summarization datasets. Data scarcity problem is not only a challenge for summarization but also for natural language processing in general. To address this, techniques such as Zero-Shot Learning and Self-Supervised Learning have emerged, which aim to create good representations so that models can handle unseen data well. In this paper, we propose a topic model-based unsupervised extractive summarization model called TES(Topic model based Extractive Summarization). Through experiments, we confirm that it performs similarly to existing models while suggesting its advantages over existing models.

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