Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position

Python implementation of Extractive Multi-document Summarization as described in Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position by Hai Cao Manh, Huong Le Thanh and Tuan Luu Minh.

Installation

1. Clone this repository.
2. Ensure packages are installed using "pip install -r requirements.txt".

Dataset

We use a dataset DUC2007 that is a famous dataset for text summarization.

Model

We propose an approach to multi-document summarization based on k-means clustering algorithm, combining with centroid-based method, maximal marginal relevance and sentence positions

Generate summaries from dataset

# Running directly from the repository: (path to save: Data/DUC_2007/folder)
methods/main_method/Kmeans_CentroidBase_MMR_SentencePosition.py --folder_to_save="folder"

Notice: if you get a path error, the following command may be helpful:
# Running directly from the repository:
export PYTHONPATH=.

Evalute results via rouge

Please replace "test" in the "system_folder" variable with system folder (ex. "folder").

# Running directly from the repository:
rouge/pyrouge_DUC_2007.py

Notice: if you get an error, you can try running the source code directly with the Pycharm IDE.

Generate summary from novel data

# Running directly from the repository:
test.py --cluster="cluster" --number_sentence_with_centroid="number_sentence_with_centroid" --number_sentence_with_mmr="number_sentence_with_mmr" --path_to_data="path_to_folder"