Implementation of Our Paper "Controllable Abstractive Sentence Summarization with Guiding Entities" in COLING 2020.
tensorflow > 2.1.0tqdmpyrougenumpynltk
Our controllable neural model with guiding entities. The original article texts are encoded with a BiLSTM layer. We utilize a pretrained BERT named entity recognition tool to extract entities from input texts. The decoder consists of two LSTMs: LSTM-L and LSTM-R. Our model starts generating the left and right part of a summary with selected entities and can guarantee that entities appear in final output summaries.
Check the README.md file for more details about sources and usage in ./data.
The preprocessed data are composed by three parts:
Source file, each line contains a sentence
Sonia Sotomayor was sworn in Saturday as the Supreme Court 's first Hispanic justice and only third female member in the top U.S. court 's 220-year history.Target file, each line contains a reference summary
Sotomayor sworn in to top U.S. court.Object file, each line contains the extracted entities, separated by a delimiter
Sonia Sotomayor <sep> Supreme court <sep> Hispanic <sep> U.S.
Set parameter and path in config.py
Tokenize with tokenizer.py
Train the model with train.py
If you find this repo helpful, please cite the following:
@inproceedings{zheng2020controllable,
title={Controllable Abstractive Sentence Summarization with Guiding Entities},
author={Zheng, Changmeng and Cai, Yi and Zhang, Guanjie and Li, Qing},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={5668--5678},
year={2020}
}