Persian Keyphrase Generation Using Sequence-to-Sequence Models

• URL: http://arxiv.org/abs/2009.12271v1
• Date: Fri, 25 Sep 2020 14:40:14 GMT
• Title: Persian Keyphrase Generation Using Sequence-to-Sequence Models
• Authors: Ehsan Doostmohammadi, Mohammad Hadi Bokaei, Hossein Sameti
• Abstract summary: Keyphrases are a summary of an input text and provide the main subjects discussed in the text. In this paper, we try to tackle the problem of keyphrase generation and extraction from news articles using deep sequence-to-sequence models.
• Score: 1.192436948211501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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