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Enhancing Semi-supevised Text Classification Using Document Summaries

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10022)


The vast amount of electronic documents available on the Internet demands for automatic tools that help people finding, organizing and easily accessing to all this information. Although current text classification methods have alleviated some of the above problems, such strategies depend on having a large and reliable set of labeled data. In order to overcome such limitation, this work proposes an alternative approach for semi-supervised text classification, which is based on a new strategy for diminishing the sensitivity to the noise contained on labeled data by means of automatic text summarization. Experimental results showed that our proposed approach outperforms traditional semi-supervised text classification techniques; additionally, our results also indicate that our approach is suitable for learning from only one labeled example per category.


  • Text classification
  • Text summarization
  • Semi-supervised learning
  • Self-training
  • Feature selection

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  • DOI: 10.1007/978-3-319-47955-2_10
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Fig. 1.
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Fig. 3.


  1. 1.

    Normally the direction of the edges is determined by the order of the sentences in the original document.

  2. 2.

    The parameter that defines the length of a summary is also known as the compression rate parameter, and represents a number that indicates the percentage of the information that we are requiring to preserve from the original document.

  3. 3.

    One disadvantage of self-training is that mistakes reinforce/strengthen themselves; it is well known that accuracies lower than random at the beginning tend to conduct to worst results in subsequent iterations.

  4. 4.


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This work was partially funded by CONACyT, project number 247870 and 258588. We appreciate the support provided by the Thematic Networks program (Language Technologies Thematic Network projects 260178 and 271622). We thank to UAM Cuajimalpa and SNI for their support.

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Correspondence to Esaú Villatoro-Tello .

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Villatoro-Tello, E., Anguiano, E., Montes-y-Gómez, M., Villaseñor-Pineda, L., Ramírez-de-la-Rosa, G. (2016). Enhancing Semi-supevised Text Classification Using Document Summaries. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham.

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