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Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs
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[2024.10.16] 🌟 GoR is released.
📌Preliminary
Environment Setup
# python==3.10
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install dgl==1.0.0+cu113 -f https://data.dgl.ai/wheels/cu113/repo.html
pip install openai==0.28
pip install pandas
pip install langchain
pip install langchain-core
pip install langchain-community
pip install langchain-experimental
pip install tiktoken
pip install tqdm
pip install bert_score
pip install rouge_score
pip install networkx
pip install faiss-gpu
pip install transformers
Dataset Preparation
QMSum WCEP Booksum GovReport SQuALITY
Save the downloaded files in the ./data/[DATASET_NAME] folder.
[!IMPORTANT]
Before running the experiment, please configure your API KEY in
"get_llm_response_via_api"inutils.py
⭐Experiments
Query Simulation and Graph Construction
Generate simulated queries and construct graphs. The constructed graphs are saved in the ./graph folder.
# DATASET Choices: qmsum, wcep, booksum, govreport, squality
# Training Set
python graph_construction.py --cuda 0 --dataset [DATASET] --train
# Test Set
python graph_construction.py --cuda 0 --dataset [DATASET]
Training Preparation
Pre-compute BERTScore and save training data in the ./training_data folder.
# DATASET Choices: qmsum, wcep, booksum, govreport, squality
python training_preparation.py --cuda 0 --dataset [DATASET]
Training
# DATASET Choices: qmsum, wcep, booksum, govreport, squality
python train.py --cuda 0 --dataset [DATASET]
Evaluation
# DATASET Choices: qmsum, wcep, booksum, govreport, squality
# Generate summary results
python eval.py --cuda 0 --dataset [DATASET]
# Evaluation
python sum_eval.py --cuda 0 --file_name ./result/[DATASET].json
Citation
@article{GoR,
title={Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs},
author={Haozhen Zhang and Tao Feng and Jiaxuan You},
journal={arXiv preprint arXiv:2410.11001},
year={2024}
}