Refactoring-Summarization

Code for our paper: "RefSum: Refactoring Neural Summarization", NAACL 2021.

We present a model, Refactor, which can be used either as a base system or a meta system for text summarization.

Outline

• Install

• Train your Refactor

• Off-the-shelf Tool: Refactoring your Models

• Dataset

• Results

1. How to Install

Requirements

• python3
• conda create --name env --file spec-file.txt
• pip3 install -r requirements.txt

Description of Codes

• main.py -> training and evaluation procedure
• model.py -> Refactor model
• data_utils.py -> dataloader
• utils.py -> utility functions
• demo.py -> off-the-shelf refactoring

2. How to Run

Hyper-parameter Setting

You may specify the hyper-parameters in main.py.

Train

python main.py --cuda --gpuid [list of gpuid] -l

Fine-tune

python main.py --cuda --gpuid [list of gpuid] -l --model_pt [model path]

Evaluate

python main.py --cuda --gpuid [single gpu] -e --model_pt [model path] --model_name [model name]

3. Off-the-shelf Refactoring

You may use our model with you own data by running

python demo.py DATA_PATH MODEL_PATH RESULT_PATH

DATA_PATH is the path of you data, which should be a file of which each line is in json format: {"article": str, "summary": str, "candidates": [str]}.

RESULT_PATH is the path of the result of which each line is a candidate summary.

4. Data

We use four datasets for our experiments.

• CNN/DailyMail -> https://github.com/abisee/cnn-dailymail
• XSum -> https://github.com/EdinburghNLP/XSum
• PubMed -> https://github.com/armancohan/long-summarization
• WikiHow -> https://github.com/mahnazkoupaee/WikiHow-Dataset

You can find the processed data for all of our experiments here. After downloading, you should put the data in ./data directory.

5. Results

CNNDM

Reranking BART

Reranking GSum

System-Combination (BART and pre-trained Refactor)

System-Combination (BART, pre-trained Refactor and GSum)

XSum

Reranking PEGASUS

PubMed

Reranking BART

WikiHow

Reranking BART