LongKey: Keyphrase Extraction for Long Documents
Abstract
In an era of information overload, manually annotating the vast and growing corpus of documents and scholarly papers is increasingly impractical. Automated keyphrase extraction addresses this challenge by identifying representative terms within texts. However, most existing methods focus on short documents (up to 512 tokens), leaving a gap in processing long-context documents. In this paper, we introduce LongKey, a novel framework for extracting keyphrases from lengthy documents, which uses an encoder-based language model to capture extended text intricacies. LongKey uses a max-pooling embedder to enhance keyphrase candidate representation. Validated on the comprehensive LDKP datasets and six diverse, unseen datasets, LongKey consistently outperforms existing unsupervised and language model-based keyphrase extraction methods. Our findings demonstrate LongKey's versatility and superior performance, marking an advancement in keyphrase extraction for varied text lengths and domains.
Cite
@article{arxiv.2411.17863,
title = {LongKey: Keyphrase Extraction for Long Documents},
author = {Jeovane Honorio Alves and Radu State and Cinthia Obladen de Almendra Freitas and Jean Paul Barddal},
journal= {arXiv preprint arXiv:2411.17863},
year = {2025}
}
Comments
Accepted for presentation at the 2024 IEEE International Conference on Big Data (IEEE BigData 2024). Code available at https://github.com/jeohalves/longkey