This repository contains the dataset, source code and trained model Comparative Opinion Summarization via Collaborative Decoding.
@inproceedings{iso22aclfindings,
title = {{C}omparative {O}pinion {S}ummarization via {C}ollaborative {D}ecoding},
author = {Hayate Iso and
Xiaolan Wang and
Stefanos Angelidis and
Yoshihiko Suhara},
booktitle = {Findings of the Association for Computational Linguistics (ACL)},
month = {May},
year = {2022}
}Please use the command below to setup and install requirements.
conda create -n cocosum python=3.8
conda activate cocosum
pip install -r requirements.txt The script prep.py automatically downloads the public TripAdvisor dataset
by Wang+(2010), and builds the CoCoTrip dataset, which includes
self-supervised training and evaluation sets.
python prep.py ./data/
head -n 50000 ./data/train_cont_all.jsonl > ./data/train_cont_50k.jsonl
head -n 1000 ./data/train_comm_pair.jsonl > ./data/train_comm_pair_1k.jsonlAll models are hosted on huggingface 🤗 model hub (https://huggingface.co/megagonlabs/).
| Model name | Task | Training setting |
|---|---|---|
| megagonlabs/cocosum-cont-self | Contrastive | Self-supervision |
| megagonlabs/cocosum-cont-few | Contrastive | Few-shot |
| megagonlabs/cocosum-comm-self | Common | Self-supervision |
| megagonlabs/cocosum-comm-few | Common | Few-shot |
You can generate contrastive and common opinion summaries by combining two base models, as referred to Collaborative-decoding. After making the CoCoTrip dataset, you can just run the following to download our pre-trained model from huggingface model hub and generate the summaries.
# Co-Decoding for Contrastive Opinion Summarization
python decode.py \
./data/ \
cont \
gen/cont/codec/ \ # directory to store generated summaries
megagonlabs/cocosum-cont-few \ # Target model
--counter_model_checkpoint megagonlabs/cocosum-cont-few \ # Counterpart model
--alpha 0.2 \ # hyper-parameter
--top_p 0.9
# Co-Decoding for Common Opinion Summarization
python decode.py \
./data/ \
comm \
gen/comm/codec \
megagonlabs/cocosum-comm-few \ # Target model
--counter_model_checkpoint megagonlabs/cocosum-cont-few \ # Counterpart model. The contrastive summarization model is used in this case.
--alpha 0.4 \ # hyper-parameter
--top_p 0.9 \
--do_ens_tgt \
--do_ens_cnt \
--ens_method add # combining the output by summing upAfter building CoCoTrip dataset, you can train the base contrastive and common opinion summarization models by running the following commands using self-supervised dataset!!
# Contrastive Summarization Model -- Self-supervision
python train.py \
./data/train_cont_50k.jsonl \
--default_root_dir ./log/cont/self \
--accumulate_grad_batches 8 \
--gradient_clip_val 1.0 \
--max_steps 50000 \
--warmup 1000 \
--val_check_interval 5000 \
--task cont \
--gpus 1
# Common Opinion Summarization -- Self-supervision
python train.py \
./data/train_comm_pair_1k.jsonl \
--default_root_dir ./log/comm/self \
--accumulate_grad_batches 8 \
--gradient_clip_val 1.0 \
--max_steps 5000 \
--warmup 100 \
--val_check_interval 500 \
--task comm \
--use_pair \
--gpus 1If you want to further train the model on top of self-supervised opinion summarization model, you can run the followings:
# Contrastive Summarization Model -- Few-Shot
python train.py \
./data/few_cont.jsonl \
--default_root_dir ./log/cont/few \
--accumulate_grad_batches 8 \
--gradient_clip_val 1.0 \
--max_steps 1000 \
--warmup 100 \
--val_check_interval 100 \
--task cont \
--ckpt log/cont/self/lightning_logs/version_0/checkpoints/ \
--gpus 1
# Common Opinion Summarization -- Few-Shot
python train.py \
./data/few_comm_pair.jsonl \
--default_root_dir ./log/comm/few \
--accumulate_grad_batches 8 \
--gradient_clip_val 1.0 \
--max_steps 1000 \
--warmup 100 \
--val_check_interval 100 \
--task comm \
--use_pair \
--ckpt log/comm/self/lightning_logs/version_0/checkpoints/ \
--gpus 1 Once you train the models for both contrastive and common opinion summarizations, it's time to generate summaries using Co-decoding!
# Co-Decoding for Contrastive Opinion Summarization
python decode.py \
./data/ \
cont \
gen/cont/codec/ \ # directory to store generated summaries
log/cont/few/lightning_logs/version_0/checkpoints/ \ # Target model
--counter_model_checkpoint log/cont/few/lightning_logs/version_0/checkpoints/ \ # Counterpart model
--alpha 0.2 \ # hyper-parameter
--top_p 0.9
# Co-Decoding for Common Opinion Summarization
python decode.py \
./data/ \
comm \
gen/comm/codec \
./log/comm/few/lightning_logs/version_0/checkpoints/ \ # Target model
--counter_model_checkpoint log/cont/few/lightning_logs/version_0/checkpoints/ \ # Counterpart model. The contrastive summarization model is used in this case.
--alpha 0.4 \ # hyper-parameter
--top_p 0.9 \
--do_ens_tgt \
--do_ens_cnt \
--ens_method add # combining the output by summing upFinally, you can evaluate your generated summaries with all the evaluation metrics by running the following command!
python evaluate.py \
./data/ \
./gen/cont/codec/outputs.json \ # path of the generated contrastive summaries
./gen/comm/codec/outputs.json # path of the generated common summariesThe repository is build based on the naacl2021-longdoc-tutorial .
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| ID | OSS Component Name | Modified | Copyright Holder | Upstream Link | License |
|---|---|---|---|---|---|
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| ID | Dataset | Modified | Copyright Holder | Source Link | License |
|---|---|---|---|---|---|
| 1 | TripAdvisor dataset | Yes | University of Virginia | source |