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International Conference on Database and Expert Systems Applications

DEXA 2019: Database and Expert Systems Applications pp 76–90Cite as

Extractive Document Summarization using Non-negative Matrix Factorization

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11707))

Abstract

Effectiveness of Non-negative Matrix Factorization (NMF) in mining latent semantic structure of text has motivated its use for single document summarization. Initial promise shown by the method provokes further research in this field to advance state-of-the-art.

In this paper, we propose two methods to improve the performance of NMF based document summarization method for mining important sentences from the text to construct summary. We use Non-negative Double Singular Value Decomposition (NNDSVD) method to initialize NMF factor matrices, which begets summary stability and improves quality. Next, we propose two novel sentence scoring methods that use parts-based representation of text obtained after NMF decomposition. Both variations exploit information contained in feature and co-efficient matrices to achieve improvement in summary quality. Quality of summaries mined by the proposed methods is evaluated for four public data-sets using standard ROUGE tool.

The proposed method is unsupervised, agnostic to the language of the document and does not use external knowledge. It is also generic, independent of domain and collection. These features of NMF based summarization along with additional advantage of speed make our method a potent candidate for online extractive summarization tool.

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Notes

  1. 1.

    We apologize for the forward reference to the data-set overview and the metric description in Sect. 5.

  2. 2.

    We create binary incidence term-sentence matrix (A) for NMF decomposition.

  3. 3.

    http://duc.nist.gov.

  4. 4.

    CNN and DailyMail corpora contain news articles and were originally constructed by [16] for the task of passage-based question answering, and later re-purposed for the task of document summarization.

  5. 5.

    However, we experimented on 533 unique documents from the data-set.

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Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, India

    Alka Khurana & Vasudha Bhatnagar

Authors
  1. Alka Khurana
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  2. Vasudha Bhatnagar
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Corresponding author

Correspondence to Alka Khurana .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  4. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Anderst-Kotsis

  5. Software Competence Center Hagenberg, Hagenberg im Mühlkreis, Austria

    A Min Tjoa

  6. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Khurana, A., Bhatnagar, V. (2019). Extractive Document Summarization using Non-negative Matrix Factorization. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-27618-8_6

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  • Online ISBN: 978-3-030-27618-8

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