PerKey: A Persian News Corpus for Keyphrase Extraction and Generation

TL;DR

PerKey is a new Persian news corpus with high-quality author-extracted keyphrases, enabling improved keyphrase extraction and generation for Persian NLP tasks, supported by evaluation of multiple techniques.

Contribution

Introduces PerKey, the first large-scale Persian news corpus with human-verified keyphrases, and evaluates various extraction methods on it.

Findings

MultipartiteRank outperforms other methods in precision and recall.

The dataset improves the development of Persian keyphrase extraction models.

Human assessment confirms the high quality of the keyphrases.

Abstract

Keyphrases provide an extremely dense summary of a text. Such information can be used in many Natural Language Processing tasks, such as information retrieval and text summarization. Since previous studies on Persian keyword or keyphrase extraction have not published their data, the field suffers from the lack of a human extracted keyphrase dataset. In this paper, we introduce PerKey, a corpus of 553k news articles from six Persian news websites and agencies with relatively high quality author extracted keyphrases, which is then filtered and cleaned to achieve higher quality keyphrases. The resulted data was put into human assessment to ensure the quality of the keyphrases. We also measured the performance of different supervised and unsupervised techniques, e.g. TFIDF, MultipartiteRank, KEA, etc. on the dataset using precision, recall, and F1-score.