We provide the source code for the paper "Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization", accepted at EMNLP'18. If you find the code useful, please cite the following paper.

@inproceedings{lebanoff-song-liu:2018,
 Author = {Logan Lebanoff and Kaiqiang Song and Fei Liu},
 Title = {Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization},
 Booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
 Year = {2018}}

• Our system seeks to summarize a set of articles (about 10) about the same topic.

• The code takes as input a text file containing a set of articles. See below on the input format of the files.

The code is written in Python (v2.7) and TensorFlow (v1.4.1). We suggest the following environment:

• A Linux machine (Ubuntu) with GPU (Cuda 8.0)
• Python (v2.7)
• TensorFlow (v1.4.1)
• Pyrouge
• NLTK

1. Clone this repo. Download this ZIP file containing the pretrained model from See et al. Move the folder pretrained_model_tf1.2.1 into the ./logs/ directory.

   $ git clone https://github.com/ucfnlp/multidoc_summarization/
   $ mv pretrained_model_tf1.2.1.zip multidoc_summarization/logs
   $ cd multidoc_summarization/logs
   $ unzip pretrained_model_tf1.2.1.zip
   $ rm pretrained_model_tf1.2.1.zip
   $ cd ..
   

2. Format your data in the following way:

   One file for each topic. Distinct articles will be separated by one blank line (two carriage returns \n). Each sentence of the article will be on its own line. See ./example_custom_dataset/ for an example.

3. Convert your data to TensorFlow examples that can be fed to the PG-MMR model.

   $ python convert_data.py --dataset=example_custom_dataset --custom_dataset_path=./example_custom_dataset/
   

4. Run the testing script. The summary files are located in the ./logs/example_custom_dataset/ directory.

   $ python run_summarization.py --dataset_name=example_custom_dataset --pg_mmr
   

This project is licensed under the BSD License - see the LICENSE.md file for details.

We gratefully acknowledge the work of Abigail See whose code was used as a basis for this project.