Redundancy Reduction of Extractive Summarization

This is the official code for the paper ['Systematically Exploring Redundancy Reduction in Summarizing Long Documents']() (AACL 2020)

In this paper, we systematically explored ways for redundancy reduction for extractive summarization on long documents.

Installation

Make sure you have python 3 and pytorch installed.

First need to install the tool rouge_papier_v2.

python setup.py install.

(This is a modified version from https://github.com/kedz/rouge_papier)

Other dependencies needed: numpy, pandas, nltk->\[word_tokenizer,stopwords\]

Data and Trained Models

We do the experiments on two scientific paper datasets, Pubmed and arXiv.

The trained models that we showed in the paper is here.

Evaluation

If you download the trained model and data, and put them in folder ./pretrained_models and ./scientific_paper_dataset/ , respectively, then you can use the following commands to evaluate the trained models.

python test.py --modelpath ./pretrained_models --datapath ./scientific_paper_dataset/ --dataset pubmed 

For different models, you need to add different arguments seeing below:

1. The original model (ExtSumLG) --model ac

2. ExtSumLG + SR Decoder --model ac_sr

3. ExtSumLG + NeuSum Decoder --model ac_neusum

4. ExtSumLG + RdLoss (beta=0.3) --model ac --beta 0.3

5. ExtSumLG + Trigram Block --model ac --use_trigram_block

6. ExtSumLG + MMR-Select (lambda =0.6) --model ac --use_mmr --lambd 0.6

7. ExtSumLG + MMR-Select+ (lambda=0.6, gamma =0.99) --model ac --use_rl --use_mmr --lambd 0.6 --gamma 0.99

Train Your Own

If you want to train your own model, you can run the following commands.

python main.py --modelpath ./pretrained_models --datapath ./scientific_paper_dataset/ --dataset pubmed 

For different models, you need to add different arguments seeing below:

1. The original model (ExtSumLG) --model ac

2. ExtSumLG + SR Decoder --model ac_sr

3. ExtSumLG + NeuSum Decoder --model ac_neusum

4. ExtSumLG + RdLoss (beta=0.3) --model ac --beta 0.3

5. ExtSumLG + MMR-Select+ (lambda=0.6, gamma=0.99) --model ac --use_rl --use_mmr --lambd 0.6 --gamma 0.99

You are free to play with different hyper parameters, which can be found in main.py.

Cite this paper

Coming Soon.