Persian Keyphrase Generation Using Sequence-to-Sequence Models
This addresses the problem of generating implicit keyphrases for natural language processing tasks like text summarization and information retrieval, but it is incremental as it applies existing sequence-to-sequence models to a specific domain.
The paper tackled keyphrase generation and extraction from news articles using deep sequence-to-sequence models, achieving significant performance improvements over conventional methods like Topic Rank, KPMiner, and KEA.
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.