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Unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering

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New Trends in Database and Information Systems (ADBIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1652))

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Abstract

Large texts are not always entirely meaningful: they might include repetitions and useless details, and might not be easy to interpret by humans. Automatic text summarization aims to simplify text by making it shorter and (possibly) more informative. This paper describes a new solution for extractive text summarization, designed to efficiently process flat (unstructured) text. It performs unsupervised frequency-based document processing to identify the candidate sentences having the highest potential to represent informative content in the document. It introduces a dedicated feature vector representation for sentences to evaluate the relative impact of different sentence terms. The sentence feature vectors are run through a partitional k-means clustering process, to build the extractive summary based on the cluster representatives. Experimental results highlight the quality and efficiency of our approach.

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Notes

  1. 1.

    We use the Porter Stemmer in our approach since it is one the most effective in the literature.

  2. 2.

    We use the WordNet lexical dictionary [3] to perform lemmatization, due to its common usage in the literature.

  3. 3.

    With respect to.

  4. 4.

    Available online: https://bit.ly/3FiaMLu.

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Authors and Affiliations

  1. School of Engineering, Lebanese American University (LAU), 36, Byblos, Lebanon

    Ali Hajjar & Joe Tekli

Authors
  1. Ali Hajjar
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  2. Joe Tekli
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Correspondence to Joe Tekli .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  5. University of Genoa, Genoa, Italy

    Barbara Catania

  6. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas-Solar

  7. University of Calabria, Rende, Italy

    Ester Zumpano

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Hajjar, A., Tekli, J. (2022). Unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_23

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  • DOI: https://doi.org/10.1007/978-3-031-15743-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15742-4

  • Online ISBN: 978-3-031-15743-1

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