Self-Supervised Multimodal Opinion Summarization

Official Pytorch implementation of the paper: "Self-Supervised Multimodal Opinion Summarization", ACL-IJCNLP, 2021.

Abstract

Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate encoder for each modality, and the text decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a multimodal training pipeline. We first pretrain the text encoder–decoder based solely on text modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained text decoder as a pivot for the homogeneous representation of multimodal data. Finally, to fuse multimodal representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by conducting experiments on Yelp and Amazon datasets.

Usage

Install dependencies

The easiest way to proceed is to create a separate conda environment.

conda create -n multimodalsum python=3.7.7
conda activate multimodalsum

Install required modules.

pip install -r requirements.txt

Download data

Our model is trained and evaluated on two review datasets: Yelp and Amazon.

Training data

Yelp

1. Download JSON and Photos from link
2. Unzip JSON
   1. Put yelp_academic_dataset_reviews to data/yelp/raw/ and name it reviews.json
   2. Put yelp_academic_dataset_business to data/yelp/raw_others/ and name it business.json
3. Put Photos to data/yelp/raw_others/

Amazon

1. Download per-category reviews and metadata from link
2. Put reviews to data/amazon/raw/
3. Unzip metadata and put them to data/amazon/raw_others/

Evaluation data

Evaluation data are located at data/yelp/test/ and data/amazon/test/

Yelp

Data are downloaded from link and some missing or mismatched business ids are fixed.

Amazon

Data are downloaded from link.

Preprocess data

Text data are tokenized, subsampled, and partitioned in order. Data preprocessing scripts are based on link and some scripts are edited.

From the root directory, execute the following command.

python -m luigi
--local-scheduler
--module preprocessing.steps Partition
--inp-dir-path=data/yelp/raw
--Subsample-min-revs=10
--Subsample-min-rev-len=35
--Subsample-max-rev-len=100
--Subsample-percentile=90
--train-part=0.9
--val-part=0.1
--GlobalConfig-out-dir-path=data/
--GlobalConfig-dataset=yelp
--log-level=INFO
--workers=4

python -m luigi
--local-scheduler
--module preprocessing.steps Partition
--inp-dir-path=data/amazon/raw
--Subsample-min-revs=10
--Subsample-min-rev-len=45
--Subsample-max-rev-len=70
--Subsample-percentile=90
--train-part=0.95
--val-part=0.05
--GlobalConfig-out-dir-path=data/
--GlobalConfig-dataset=amazon
--log-level=INFO
--workers=4

Multimodal preprocess data

Text, Image, and Table data for multimodal learning are prepared.

From the root directory, execute the following command.

python multimodal_preprocessing/multimodal_preprocessing.py --dataset yelp
python multimodal_preprocessing/multimodal_preprocessing.py --dataset amazon

Model train (multimodal training pipeline)

MultimodalSum consists of text encoder, image encoder, table encoder, and text decoder.

• Text encoder and decoder are based on BART and implemented based on the Transformers library from Hugging Face.
• Image encoder is based on ResNet101.
• Table encoder is based on data-to-text research.

To resolve the inherent heterogeneity of multimodal data, MultimodalSum is trained along the multimodal training pipeline. Multimodal training pipeline consists of three steps.

• Text modality pretraining (Pretrain text encoder and decoder)
• Other modalities pretraining
  • Img modality pretraining (Pretrain image encoder)
  • Table modality pretraining (Pretrain table encoder)
• Multiple modalities training (Train MultimodalSum)

From the root directory, execute the following command.

# 0) Further pretrain BART using the entire training review corpus
python src/bart-review.py

# 1) Text modality pretraining
OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=4 src/text_pretrain.py

# 2) Other modalities pretraining
OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=2 src/img_pretrain.py
OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=2 src/table_pretrain.py

# 3) Multiple modalities training
OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=8 src/multimodal_train.py

Model weights are saved in ckpt/ and log files are saved in log/.

Model evaluation

From the root directory, execute the following command.

python src/test.py

Generated summaries

Generated summaries from MultimodalSum on Yelp and Amazon test data are included in output/.

Citation

@inproceedings{im-etal-2021-self,
    title = "Self-Supervised Multimodal Opinion Summarization",
    author = "Im, Jinbae  and
      Kim, Moonki  and
      Lee, Hoyeop  and
      Cho, Hyunsouk  and
      Chung, Sehee",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.33",
    doi = "10.18653/v1/2021.acl-long.33",
    pages = "388--403"
}