Query Based Abstractive Summarization

Overview

Implementation of diversity based attention model for query based abstractive summarization task. The implementation is adapted from the seq2seq package in Tensorflow.

The implementation is based on this work.

Diversity based attention models for Query based abstractive summarization

Preksha Nema, Mitesh M. Khapra, Anirban Laha, Balaraman Ravindran

ACL, 2017

Requirements:

Data Download and Preprocessing

* cd src/dataextraction_scripts
* The model will extract the data for the categories mentioned in file debatepedia_categories
* sh extract_data.sh

### To use the existing extracted dataset in dataset folder:
* cd src/dataextraction_scripts
* python make_folds.py ../../data <number_of_folds> <new_dir_for_10_folds> 
* By default run : python make_folds.py ../../data 10 ../../data

Get the Glove embeddings:

mkdir Embedding
cd Embedding
wget http://nlp.stanford.edu/data/glove.840B.300d.zip
unzip glove.840B.300d.zip
echo 2196017 300 > temp_metadata
cat temp_metadata glove.840B.300d.txt > embeddings
rm temp_metadata

Configuration file:

 * The hyper parameters could be changed in the config.txt file.
 * The influence of eah hyperarameter have been explained in detail in the comments in config.txt

Train the model

  sh ./train.sh config.txt

Inference:

 test.sh will refer to the config.txt to initialize the graph that will be used for creating the graph for inference.
 sh ./test.sh config.txt output/test_final_results

abdarhman/diversity_based_attention