This is code for paper: MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction Data is from OpenNMT-kpg-release and SIFRank. (Inspec, DUC2001, SemEval2017 are from SIFRank).
Python 3.7
nltk 3.4.3
StanfordCoreNLP 3.9.1.1
torch 1.1.0
allennlp 0.8.4
pke 1.8.1
transformer 4.14.1
CUDA version 10.2
We use run.sh script to run MDERank.
sh run.sh
--checkpoint is the model used for predictions. Initial MDERank use bert-base-uncased.
If you use this code, please cite this paper
@article{DBLP:journals/corr/abs-2110-06651,
author = {Linhan Zhang and
Qian Chen and
Wen Wang and
Chong Deng and
Shiliang Zhang and
Bing Li and
Wei Wang and
Xin Cao},
title = {MDERank: {A} Masked Document Embedding Rank Approach for Unsupervised
Keyphrase Extraction},
journal = {CoRR},
volume = {abs/2110.06651},
year = {2021},
url = {https://arxiv.org/abs/2110.06651},
eprinttype = {arXiv},
eprint = {2110.06651},
timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2110-06651.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}