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Persian Automatic Text Summarization Based on Named Entity Recognition


In this paper, we propose an unsupervised method for summarizing Farsi texts based on our neural named entity recognition (NER) system. This method consists of three phases: training a supervised NER model, recognizing named entities of the text, and generating a summary. The proposed method is an unsupervised extractive single-document summarization method. Although the proposed method is language independent, we focus on Farsi text summarization in this work. Firstly, we produce a word embedding based on Hamshahri2 corpus. Secondly, we train a neural network on Arman NER corpus. Then, the proposed algorithm ranks the sentences of the text based on the named entities in each sentence and produces the summary. Finally, the proposed method is evaluated on Pasokh single-document data set using the ROUGE evaluation measure. Without using any handcrafted features, our proposed method achieves state-of-the-art results. We compared our unsupervised method with the best supervised Farsi methods, and we achieved an overall improvement of ROUGE-2 recall score of 10.2%.

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Correspondence to Mohammad Fakhredanesh.

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Khademi, M.E., Fakhredanesh, M. Persian Automatic Text Summarization Based on Named Entity Recognition. Iran J Sci Technol Trans Electr Eng (2020).

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  • Extractive summarization
  • Named entity recognition
  • Continuous vector space
  • Word embedding