Code for ACL 2020 paper: Schumann et al. "Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction"
tested with python version 3.7
pip install -r requirements.txt
Download Gigaword for headline generation from https://github.com/harvardnlp/sent-summary.
Download the pretrained weights from here
Move model.ckpt-282245.data-00000-of-00001 to lm/outputs/title_forward/ckpt/
Move model.ckpt-311955.data-00000-of-00001 to lm/outputs/title_backward/ckpt/
Move s2v_title.npy to data/sent2vec/
Set correct paths in configs/hc_title_8.yaml
Change char_length parameters in yaml file to control the character length generated
python run.py --input_file data/summary/sumdata/Giga/input.txt --config configs/hc_title_8.yaml --output_dir outputs/hc_title_8
You can split the input file along its lines to run them in parallel with the arguments --from_line 100 --to_line 200
python outputs/evaluate.py --output_dir outputs/hc_title_8/ --reference_file data/summary/sumdata/Giga/task1_ref0.txt
| Model | Rouge-F1-1 | Rouge-F1-2 | Rouge-F1-L |
|---|---|---|---|
| HC_title_8_50_char | 25.30 | 9.25 | 23.43 |
| HC_title_10_60_char | |||
| HC_title_13_80_char |
For custom data you have to create a new vocabulary file with lm/create_vocab.py and new idf counts with cos/tfidf.py
python lm/language_model.py --input_file path_to_data/train.title.txt --valid_file path_to_data/valid.title.filter.small.txt --vocab_file lm/vocabs/title.vocab --config_file lm/configs/title_forward.yaml --save_dir lm/outputs/title_forward
same for backward language model with config lm/configs/title_backward.yaml
install sent2vec from https://github.com/epfml/sent2vec
./fasttext sent2vec -input data/train.title.txt -output title -minCount 5 -dim 700 -epoch 20 -lr 0.2 -wordNgrams 1 -loss ns -neg 10 -thread 20 -t 0.000005 -dropoutK 4 -minCountLabel 20 -bucket 4000000 -maxVocabSize 70000 -numCheckPoints 1
convert to numpy array with correct vocab order:
python scripts/sent2vec_model_to_numpy.py --s2v_model title.bin --vocab_file title.vocab --output_file embeddings/s2v_title.npy
Please cite the following paper if you use this code:
@inproceedings{schumann-etal-2020-discrete,
title = "Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction",
author = "Schumann, Raphael and Mou, Lili and Lu, Yao and Vechtomova, Olga and Markert, Katja",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
pages = "5032--5042",}