#GLEAM# The source code of global generative topic fusion with local sentence generation encoding for abstractive summarization.
Requirement:   Tensorflow ; Bazel

#Description:#

Global representation generation

Before running GLEAM, the VAE topic model need to be trained on corpus and export the corresponding document vectors.

1. vocab_headline.py generate the vocabulary of corpus.
   usage: python vocab_headline.py <souce_dir> <target_dir> <source_dir> : directory of corpus.
   <target_dir> : output directory of vocabulary file.
2. batch_text_vector.py process corpus for training and evaluating.
   usage: python batch_text_vector.py <souce_dir> <target_dir> <vocab_path>
   <source_dir> : directory of corpus.
   <target_dir> : output directory of vocabulary file.
   <vocab_path> : vocabulary file path.
3. batch_nvdm.py training NVDM on the corpus processed above (which needs to be placed at 'train' folder).
   Training : python NVDM.py
   Testing and generate the document vectors: Python NVDM.py --test_data_dir <test_dir> --store_data_dir <store_dir> --test True
   <test_dir> : test corpuse directory.
   <store_dir>: directory where to store the document vectors.
   【The above 1-4 procedure need to run on both training set and test set】

Generating summarization

Recommending runing with GPU.
5. selecting GPU device to run the model:
export CUDA_VISIBLE_DEVICES = <gpu_id>
<gpu_id> : Your cuda gpu id.
6. Build the project:
bazel build -c opt --config=cuda textsum/...
7. Training:
bazel-bin/textsum/seq2seq_attention
--mode=train
--article_key=article
--abstract_key=abstract
--data_path=data/train/*
--vocab_path=data/vocabulary/vocab
--log_root=textsum/log_root
--train_dir=textsum/log_root/train**
8. Decoding:
bazel-bin/textsum/seq2seq_attention
--mode=decode
--article_key=article
--abstract_key=abstract
--data_path=data/duc_test_two_vector/*
--vocab_path=data/vocabulary/vocab
--log_root=textsum/log_root
--decode_dir=textsum/log_root/decode
--beam_size=8**