Single Document Extractive Summarization Based on Deep Neural Networks Using Linguistic Analysis Features


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 8, pp. 343-348, Aug. 2019
https://doi.org/10.3745/KTSDE.2019.8.8.343, Full Text:
Keywords: Single Document Summarization, Extractive Summarization, Linguistic Analysis Features, Deep Neural Networks
Abstract

In recent years, extractive summarization systems based on end-to-end deep learning models have become popular. These systems do not require human-crafted features and adopt data-driven approaches. However, previous related studies have shown that linguistic analysis features such as part-of-speeches, named entities and word’s frequencies are useful for extracting important sentences from a document to generate a summary. In this paper, we propose an extractive summarization system based on deep neural networks using conventional linguistic analysis features. In order to prove the usefulness of the linguistic analysis features, we compare the models with and without those features. The experimental results show that the model with the linguistic analysis features improves the Rouge-2 F1 score by 0.5 points compared to the model without those features.


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Cite this article
[IEEE Style]
G. H. Lee and K. J. Lee, "Single Document Extractive Summarization Based on Deep Neural Networks Using Linguistic Analysis Features," KIPS Transactions on Software and Data Engineering, vol. 8, no. 8, pp. 343-348, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.8.343.

[ACM Style]
Gyoung Ho Lee and Kong Joo Lee. 2019. Single Document Extractive Summarization Based on Deep Neural Networks Using Linguistic Analysis Features. KIPS Transactions on Software and Data Engineering, 8, 8, (2019), 343-348. DOI: https://doi.org/10.3745/KTSDE.2019.8.8.343.