Code for "Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization"
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AttentionUnit.py first commit Jun 28, 2017
DataLoader.py first commit Jun 28, 2017
LstmUnit.py first commit Jun 28, 2017
MleTrain.py first commit Jun 28, 2017
OutputUnit.py first commit Jun 28, 2017
README.md Update README.md May 13, 2018
SeqUnit.py update Feb 6, 2018
Unit.py first commit Jun 28, 2017

README.md

Semantic Relevance Based Text Summarization Model

Code for "Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization" The codes are also used for "A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification"

Requirements

  • Tensorflow r1.0.1
  • Python 3.5
  • CUDA 8.0 (For GPU)
  • ROUGE

Data

The dataset in the paper is Large Scale Chinese Short Text Summarization (LCSTS). To preprocess the data, please split the sentences into characters, and transform the characters into numbers (ids).

Run

python3 MleTrain.py

Cite

If you use this code for your research, please cite the paper this code is based on: Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization:

@inproceedings{MaEA2017,
	author    = {Shuming Ma and Xu Sun and Jingjing Xu and Houfeng Wang and Wenjie Li and Qi Su},
	title     = {Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization},
	booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational
	Linguistics, {ACL} 2017, Vancouver, Canada, July 30 - August 4, Volume
	2: Short Papers},
	pages     = {635--640},
	year      = {2017}
}