We often need to simplify the official bib that consists of many information into a shorter version that only maintains necessary information (e.g., author, title, conference/journal name and etc) due to page limitation.

We introduce SimBiber, a simple tool in Python to simplify them automatically. Hope it's helpful for you.

We also highly recommend another wonderful tool for you Rebiber, which is a tool for normalizing bibtex with official info.

Tips: If you use first Rebiber and then Simbiber, you can get a better experience.

SimBiber is a fairly new project and it is under active development. We hope that it will be quite useful in a variety of cases, but there is no guarantee that the results it produces will necessarily be strictly compliant with the official specification.

So you'd better check the accuracy of simplified bib files again.

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• 2023.02.33
  • Fix some bugs about with -keep parameter.
• 2021.05.02
  • Fix some bugs about without -keep parameter.
• 2021.05.01
  • Support to customize the keys you want to reserve.
• 2021.04.23
  • Support IJCAI (Survey Track).
  • Unified README.
• 2021.04.11
  • Support to pip install.
  • Simplify input args.
  • Add disclaimer.
• 2021.03.02
  • Fix some bugs if remove duplications.
• 2021.02.15
  • Fix a bug simplify ACL (like EACL) conference to ACL.
  • Support ACL Findings and EMNLP findings.
• 2021.01.21
  • Support to remove duplication if your bib has some bibitems with same title. (automatically choose Conference citation)
  • Fix some bugs about some conferences.
  • Add more categories of conferences. (now support 113 conferences)
• 2021.01.11
  • Fix a bug if output path is the same as input path.
  • Support to remove duplication if your bib has both of arXiv or Conference citation.
  • Support to simplify files by folder.
  • Support to use default output path.
  • Add more categories of conferences. (now support 112 conferences)
• 2021.01.08 We fix a bug if booktitle contains { or } and add more categories of conferences. (now support 105 conferences)
• 2021.01.06 We fix a few minor bugs and add more categories of conferences. (now support 84 conferences)
• 2021.12.31 We build the first version and release it.

git clone https://github.com/MLNLP-World/Simbiber.git
cd Simbiber/
pip install -e .

OR

pip install simbiber

If you would like to use the latest github version with more bug fixes, please use the first installation method.

Finally, if you run simbiber without any args, you get the following result, then the installation is successful!

simbiber -i [input bib path] -o [output bib path] 

Tips: All path args support absolute and relative paths

An example simplified output entry with the official information (The forms of bibitem like xxx="..." or xxx={...} are both supported):

@inproceedings{li-etal-2019-survey,
    title = "A Sophisticated Survey about Chinese Poem and Beers",
    author = "Li, Bai  and
     Ha, Pi  and
     Jin, Shibai  and
     Xue, Hua  and
     Mao, Tai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-1214",
    doi = "10.18653/v1/D19-1214",
    pages = "2078--2087",
    abstract = "Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely tied and the slots often highly depend on the intent. In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guiding the slot filling. In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.",
}

An example simplified output entry from the official information:

@inproceedings{li-etal-2019-survey,
    author = {Li, Bai  and
     Ha, Pi  and
     Jin, Shibai  and
     Xue, Hua  and
     Mao, Tai},
    booktitle = {Proc. of EMNLP},
    title = {A Sophisticated Survey about Chinese Poem and Beers},
    year = {2019}
}

The config dir contains a list of converted json files of the mapper between official full name and simplified name.

You can manually add any conferences from DBLP to config map.

Take ICLR as an example:

• Step 1: Go to DBLP
• Step 2: Find the full name of Conference
• Step 3: Add map to config/ML.json or parserConfig.json(You should specify the config path)

{"International Conference on Learning Representations": "ICLR"}

Please email Libo Qin or Qiguang Chen to create Github issues here if you have any questions or suggestions.

And we welcome you to join us and update conferences at https://docs.qq.com/sheet/DWFF1aWlVV1hISU12?tab=h2idmj

Thanks to the contributors: