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Persian keyphrase generation using sequence-to-sequence models
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Persian keyphrase generation using sequence-to-sequence models

Doostmohammadi, E

Persian keyphrase generation using sequence-to-sequence models

Doostmohammadi, E ; Sharif University of Technology | 2019

707 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/IranianCEE.2019.8786505
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. Keyphrases are a very short summary of an input text and provide the main subjects discussed in the text. Keyphrase extraction is a useful upstream task and can be used in various natural language processing problems, for example, text summarization and information retrieval, to name a few. However, not all the keyphrases are explicitly mentioned in the body of the text. In real-world examples there are always some topics that are discussed implicitly. Extracting such keyphrases requires a generative approach, which is adopted here. In this paper, we try to tackle the problem of keyphrase generation and extraction from news articles using deep sequence-to-sequence models. These models significantly outperform the conventional methods such as Topic Rank, KPMiner, and KEA in the task of keyphrase extraction 1
  6. Keywords:
  7. Keyphrase Generation ; Sequence-to-sequence Learning ; Extraction ; Natural language processing systems ; Conventional methods ; Key-phrase ; Keyphrase extraction ; NAtural language processing ; News articles ; Sequence learning ; Sequence models ; Text summarization ; Recurrent neural networks
  8. Source: 27th Iranian Conference on Electrical Engineering, ICEE 2019, 30 April 2019 through 2 May 2019 ; 2019 , Pages 2010-2015 ; 9781728115085 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/8786505