Learning Opinion Summarizers by Selecting Informative Reviews
This repository contains the codebase and the dataset for the corresponding EMNLP 2021 paper. Please star the repository and cite the paper if you find it useful.
SelSum is a probabilistic (latent) model that selects informative reviews from large collections and subsequently summarizes them as shown in the diagram below.
AmaSum is the largest abstractive opinion summarization dataset, consisting of more than 33,000 human-written summaries for Amazon products. Each summary is paired, on average, with more than 320 customer reviews. Summaries consist of verdicts, pros, and cons, see the example below.
Verdict: The Olympus Evolt E-500 is a compact, easy-to-use digital SLR camera with a broad feature set for its class and very nice photo quality overall.
Pros:
- Compact design
- Strong autofocus performance even in low-light situations
- Intuitive and easy-to-navigate menu system
- Wide range of automated and manual features to appeal to both serious hobbyists and curious SLR newcomers
Cons:
- Unreliable automatic white balance in some conditions
- Slow start-up time when dust reduction is enabled
- Compatible Zuiko lenses don’t indicate focal distance
1. Setting up
1.1. Environment
The easiest way to proceed is to create a separate conda environment with Python 3.7.0.
conda create -n selsum python=3.7.0
Further, install PyTorch as shown below.
conda install -c pytorch pytorch=1.7.0
In addition, install the essential python modules:
pip install -r requirements.txt
The codebase relies on FairSeq. To avoid version conflicts, please download our version and store it to ../fairseq_lib. Please follow the installation instructions in the unzipped directory.
1.2. Environmental variables
Before running scripts, please add the environmental variables below.
export PYTHONPATH=../fairseq_lib/.:$PYTHONPATH
export CUDA_VISIBLE_DEVICES=0,1,2,3
export MKL_THREADING_LAYER=GNU
1.3. Data
The dataset in various formats is available in the dataset folder. To run the model, please binarize the fairseq specific version.
1.4. Checkpoints
We also provide the checkpoints of the trained models. These should be allocated to artifacts/checkpoints.
2. Training
2.1. Posterior and Summarizer training
First, the posterior and summarizer need to be trained. The summarizer is initialized using the BART base model, please download the checkpoint and store it to artifacts/bart. Note: please adjust hyper-parameters and paths in the script if needed.
bash selsum/scripts/training/train_selsum.sh
Please note that REINFORCE-based loss for the posterior training can be negative as the forward pass does not correspond to the actual loss function. Instead, the loss is re-formulated to compute gradients in the backward pass (Eq. 5 in the paper).
2.2. Selecting reviews with the Posterior
Once the posterior is trained (jointly with the summarizer), informative reviews need to be selected. The script below produces binary tags indicating selected reviews.
python selsum/scripts/inference/posterior_select_revs.py --data-path=../data/form \
--checkpoint-path=artifacts/checkpoints/selsum.pt \
--bart-dir=artifacts/bart \
--output-folder-path=artifacts/output/q_sel \
--split=test \
--ndocs=10 \
--batch-size=30
The output can be downloaded and stored to artifacts/output/q_sel.
2.3. Fitting the Prior
Once tags are produced by the posterior, we can fit the prior to approximate it.
bash selsum/scripts/training/train_prior.sh
2.4. Selecting Reviews with the Prior
After the prior is trained, we select informative reviews for downstream summarization.
python selsum/scripts/inference/prior_select_revs.py --data-path=../data/form \
--checkpoint-path=artifacts/checkpoints/prior.pt \
--bart-dir=artifacts/bart \
--output-folder-path=artifacts/output/p_sel \
--split=test \
--ndocs=10 \
--batch-size=10
The output can be downloaded and stored to artifacts/output/p_sel.
3. Inference
3.1. Summary generation
To generate summaries, run the command below:
python selsum/scripts/inference/gen_summs.py --data-path=artifacts/output/p_sel/ \
--bart-dir=artifacts/bart \
--checkpoint-path=artifacts/checkpoints/selsum.pt \
--output-folder-path=artifacts/output/p_summs \
--split=test \
--batch-size=20
The model outputs are also available at artifacts/summs.
3.2. Evaluation
For evaluation, we used a wrapper over ROUGE and the CoreNLP tokenizer.
The tokenizer requires the CoreNLP library to be downloaded. Please unzip it to the artifacts/misc folder. Further, make it visible in the classpath as shown below.
export CLASSPATH=artifacts/misc/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar
After the installations, please adjust the paths and use the commands below.
cat "${GOLD_FILE_PATH}" | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > "${GOLD_FILE_PATH}.tokenized"
# rouge evaluation
files2rouge "${GOLD_FILE_PATH}.tokenized" "${GEN_FILE_PATH}.tokenized"
“>
GEN_FILE_PATH=artifacts/summs/test.verd
GOLD_FILE_PATH=../data/form/eval/test.verd
# tokenization
cat "${GEN_FILE_PATH}" | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > "${GEN_FILE_PATH}.tokenized"
cat "${GOLD_FILE_PATH}" | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > "${GOLD_FILE_PATH}.tokenized"
# rouge evaluation
files2rouge "${GOLD_FILE_PATH}.tokenized" "${GEN_FILE_PATH}.tokenized"
Citation
@inproceedings{bražinskas2021learning,
title={Learning Opinion Summarizers by Selecting Informative Reviews},
author={Arthur Bražinskas and Mirella Lapata and Ivan Titov},
booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year={2021},
}
License
Codebase: MIT
Dataset: non-commercial
Notes
- Occasionally logging stops being printed while the model is training. In this case, the log can be displayed either with a gap or only at the end of the epoch.
- SelSum is trained with a single data worker process because otherwise cross-parallel errors are encountered.
