Skip to content

Latest commit

 

History

History
92 lines (72 loc) · 3.07 KB

README.md

File metadata and controls

92 lines (72 loc) · 3.07 KB
Raw

MetaKP

🖋 Di Wu*, Xiaoxian Shen*, and Kai-Wei Chang

We introduce on-demand keyphrase generation, a novel paradigm that requires keyphrase predictions to conform to specific high-level goals or intents.

We release MetaKP, a large-scale benchmarking dataset covering four datasets, 7500 documents, and 3760 goals from the news and biomedical text domain.

Overview

Setup

Data

The MetaKP dataset is officially released in the data/ folder. Please download and uncompress the data to the data/ folder. You can use the get_data.sh script to uncompress all the files in the corresponding folder.

cd data
bash get_data.sh
cd ..

We cover datas in KPTimes, DUC2001, KPBiomed, and Pubmed. Each file has the following format:

  • humanvalid_processed_release.json: This is a human valided data created using our pipeline.
  • rejection_augmented_release.json: This is the rejection augmented version, for a document, all the negative goals are appended after positive goals.

Each file contains the following fields:

  • id: the unique id for each goal, keyphrase pair.
  • title: title of the document.
  • document: document body.
  • goal: the intended goal.
  • keyphrases: a list of keyphrases that could be generated using the goal.

Environment

We recommend using a conda environment for the project. You may follow the steps below to set up.

conda create --name metakp python==3.8
conda activate metakp
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt

Fine-tuning

Pre-processing

Prepare the data by running cd data ; python process_seq2seq.python . The script will preprocess specified datasets.

Train

  • cd fine-tuning
  • Modify the parameters in run_train_bart_multitask.sh or run_train_flan_t5_with_rejection.sh.
  • bash run_train_bart_multitask.sh or bash run_train_flan_t5_with_rejection.sh

Inference and Evaluation

bash run_test.sh

Prompting

Inference and Evaluation

  • Run zero-shot goal rejection experiment
bash run_query_reject_zero_shot.sh
  • Run zero-shot on-demand keyphrase generation experiment
bash run_query_kp_zero_shot.sh
  • Run self-consistency prompting on-demand keyphrase generation experiment
bash run_query_kp_zero_shot_sample.sh

By default, the threshold for scoring keyphrases in self-consistency prompting evaluation is 0.3.

If you find this work helpful, please consider citing

@article{wu2024metakp,
      title={MetaKP: On-Demand Keyphrase Generation}, 
      author={Di Wu and Xiaoxian Shen and Kai-Wei Chang},
      year={2024},
      eprint={2407.00191},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.00191}, 
}