# PerKey: A Persian News Corpus for Keyphrase Extraction and Generation

arXiv: https://arxiv.org/abs/2009.12269 · HTML: https://mlsanity.com/paper/2009.12269/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization](https://mlsanity.com/paper/2109.04098/) (2021)
- [PerkwE_COQA: Enhanced Persian Conversational Question Answering by combining contextual keyword extraction with Large Language Models](https://mlsanity.com/paper/2404.05406/) (2024)

## Abstract (arXiv)

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.