Persian Keyphrase Generation Using Sequence-to-Sequence Models

TL;DR

This paper explores the use of deep sequence-to-sequence models for generating keyphrases from news articles, effectively capturing both explicit and implicit topics, and outperforms traditional extraction methods.

Contribution

It introduces a generative approach using sequence-to-sequence models for keyphrase extraction, addressing implicit keyphrases and surpassing conventional methods.

Findings

Sequence-to-sequence models outperform traditional methods.

The approach captures implicit keyphrases.

Significant improvement in keyphrase extraction accuracy.

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.