One implementation of the paper Exploiting Discourse-Level Segmentation for Extractive Summarization
, and the EDU level segmentation part is updated by using our new DMRST_Parser.
- Please note that the
SegMerge_Infer.py
is only for building the EDU segments, not the summarization part. - Users can apply it to process the input text, and obtain the EDU segments, as well as the merged segments for extractive summarization.
- This repo and the pre-trained model are only for research use. Please cite the papers if they are helpful.
The model training and inference scripts were tested on following libraries and versions:
- pytorch==1.7.1
- transformers==4.8.2
- Download the code from the repo DMRST_Parser.
- Put the
SegMerge_Infer.py
to the repo folder. - Put your text paragraph to the file
./data/text_for_inference.txt
. - Pre-trained model checkpoint should be downloaded and saved at
./depth_mode/Savings/
(see repoDMRST_Parser
). - Run the script
SegMerge_Infer.py
to obtain the EDU segmentationg and merge result. See the script for detailed model output. - We recommend users to run the parser on a GPU-equipped environment.
If the work is helpful, please cite our papers in your publications, reports, slides, and docs.
@inproceedings{liu2019exploiting,
title={Exploiting discourse-level segmentation for extractive summarization},
author={Liu, Zhengyuan and Chen, Nancy},
booktitle={Proceedings of the 2nd Workshop on New Frontiers in Summarization},
pages={116--121},
year={2019}
}
@inproceedings{liu-etal-2021-dmrst,
title = "{DMRST}: A Joint Framework for Document-Level Multilingual {RST} Discourse Segmentation and Parsing",
author = "Liu, Zhengyuan and Shi, Ke and Chen, Nancy",
booktitle = "Proceedings of the 2nd Workshop on Computational Approaches to Discourse",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.codi-main.15",
pages = "154--164",
}