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README.md

seq2seq-keyphrase

Introduction

This is an implementation of Deep Keyphrase Generation based on CopyNet.

One training dataset (KP20k), five testing datasets (KP20k, Inspec, NUS, SemEval, Krapivin) and one pre-trained model are provided.

Note that the model is trained on scientific papers (abstract and keyword) in Computer Science domain, so it's expected to work well only for CS papers.

About How to Reproduce

My model was trained on Ubuntu 14.04, Python 2.7, Theano 0.8, CUDA 8.0 and cuDNN v5.

I found that the development of Theano has been terminated and I also encountered problem in running after update to newer version of CUDA and Theano (now it's CUDA 9.0 and Theano 1.0. I really regret doing so lol).

I am implementing a PyTorch version (here) and it will be released soon. Stay tuned.

Run

Entry and Settings

The main entry of the project is placed in keyphrase/keyphrase_copynet.py

All the primary settings are stored in keyphrase/config.py. Training and Prediction load settings from setup_keyphrase_all() and Evaluation loads the setting from setup_keyphrase_baseline().

Some scripts for data processing are placed in keyphrase/dataset/.

Before running...

Before running the code, please download this seq2seq-keyphrase.zip, unzip it to the project directory and overwrite the Experiment/ and dataset/.

Experiment/ contains one pre-trained copy-seq2seq model (experiments.keyphrase-all.one2one.copy.id=20170106-025508.epoch=4.batch=1000.pkl) used in the paper, based on which you can extract keyphrases for your own corpus. Besides there are some output examples in this folder.

dataset/ contains three folders.

  • punctuation-20000validation-20000testing contains the pickled data for training/validation.
  • testing-data contains the original testing datasets, and they are further processed into folder baseline-data.
  • baseline-data stores the cleaned and processed testing datasets, and will be used during predicting and evaluating. Specifically, for each dataset, there's one text folder contains the content of paper after POS-tagging, and another keyphrase folder contains the ground-truth keyphrases, listed one phrase per line.

Training

If you want to train a new model, set config['do_train'] = True and config['trained_model'] = '' in keyphrase/config.py.

If the config['trained_model'] is not empty, it will load the trained model first and resume training.

Also, there are some parameters you can try out, like config['copynet'] = False means to train a normal GRU-based Seq2seq model.

Predicting keyphrases

Set config['do_predict'] = True and config['testing_datasets']=['data_set1', 'data_set2' ...] (datasets you wanna extract). The program will load the text from dataset/baseline-data/ first, and save the prediction results into config['predict_path']/predict.generative.dataset_name.pkl and the extracted phrases into dataset/keyphrase/prediction/.

Similarly, there are many parameters to tune the prediction of keyphrase.

If you want to extract keyphrases from your own data using our model, you need to put your data in baseline-data following the same format, and implement a simple class in keyphrase/dataset/keyphrase_test_dataset.py.

Test

Set config['do_evaluate'] = True and you'll see a lot of print-outs in the console and reports in directory config['predict_path']. Please be aware that this result is only for developing and debugging and it's slightly different from the reported result.

Evaluation (to reproduce the results in the paper)

The performances reported in the paper is done by keyphrase/baseline/evaluate.py. It loads the phrases from dataset/keyphrase/prediction/ and evalutes them with metrics such as Precision, Recall, F-score, Bpref, MRR etc.

Note that the setting of evaluation is different from the settings used in training/predicting and don't be confused. It is loaded by calling setup_keyphrase_baseline() in config.py. Also if you want to reproduce the result of present keyphrase prediction (Section 5.1 of the paper), please set config['predict_filter'] and config['target_filter'] to 'appear-only' (line 292,293). Similarly, set them to 'non-appear-only' for reproducing absent keyphrase prediction (Section 5.2 of the paper).

You can find the awesome baseline implementations from Kazi Saidul Hasan (TfIdf, TextRank, SimpleRank, ExpandRank) and Alyona Medelyan (Maui and KEA). My keyphrase outputs are included in the seq2seq-keyphrase.zip (seq2seq-keyphrase-release/dataset/keyphrase/prediction/).

Data

The training data mentioned above is pickled. You can download here: seq2seq-keyphrase.zip.

If you are just interested in using the KP20k dataset, you can get the data as well: kp20k.zip.

The KP20k dataset is released in JSON format. Each data point contains the title, abstract and keywords of a paper.

Part #(data)
Training 530,809
Validation 20,000
Test 20,000

The raw dataset (without filtering noisy data) is also provided. Please download here.

Cite

If you use the code or datasets, please cite the following paper:

Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He, Peter Brusilovsky and Yu Chi. Deep Keyphrase Generation. 55th Annual Meeting of Association for Computational Linguistics, 2017. [PDF] [arXiv]

@InProceedings{meng-EtAl:2017:Long,
  author    = {Meng, Rui  and  Zhao, Sanqiang  and  Han, Shuguang  and  He, Daqing  and  Brusilovsky, Peter  and  Chi, Yu},
  title     = {Deep Keyphrase Generation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {582--592},
  url       = {http://aclweb.org/anthology/P17-1054}
}