Learning-Free Unsupervised Extractive Summarization Model

Myeongjun Jang, Pilsung Kang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Text summarization is an information condensation technique that abbreviates a source document to a few representative sentences with the intention to create a coherent summary containing relevant information of source corpora. This promising subject has been rapidly developed since the advent of deep learning. However, summarization models based on deep neural network have several critical shortcomings. First, a large amount of labeled training data is necessary. This problem is standard for low-resource languages in which publicly available labeled data do not exist. In addition, a significant amount of computational ability is required to train neural models with enormous network parameters. In this study, we propose a model called Learning Free Integer Programming Summarizer (LFIP-SUM), which is an unsupervised extractive summarization model. The advantage of our approach is that parameter training is unnecessary because the model does not require any labeled training data. To achieve this, we formulate an integer programming problem based on pre-trained sentence embedding vectors. We also use principal component analysis to automatically determine the number of sentences to be extracted and to evaluate the importance of each sentence. Experimental results demonstrate that the proposed model exhibits generally acceptable performance compared with deep learning summarization models although it does not learn any parameters during the model construction process.

Original languageEnglish
Article number9321308
Pages (from-to)14358-14368
Number of pages11
JournalIEEE Access
Publication statusPublished - 2021


  • Text summarization
  • integer linear programming
  • natural language processing
  • sentence representation vector

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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