Modification of the KeyBERT method to extract keywords and keyphrases using chunks. This provides better results, especialy when handling long documents.
ChunkeyBert is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings for unsupervised keyphrase extraction from text documents. ChunkeyBert is a modification of the KeyBERT method to handle documents with arbitrary length with better results. ChunkeyBERT works by chunking the documents and uses KeyBERT to extract candidate keywords/keyphrases from all chunks followed by a similarity based selection stage to produce the final keywords for the entire document. ChunkeyBert can use any document chunking method as long as it can be wrapped in a simple function, however it can also work without a chunker and process the entire document as a single chunk. ChunkeyBert works with any configuration of KeyBERT and can handle batches of documents.
Install from PyPI using pip (preferred method):
pip install chunkey-bert
Very limited experimental results and demonstration of the library on a small number of documents is available at https://nbviewer.org/github/yaniv-shulman/chunkey-bert/tree/main/src/experiments/.
Contributions and feedback are most welcome. Please see CONTRIBUTING.md for further details.
FAQs
Modification of the KeyBERT method to extract keywords and keyphrases using chunks. This provides better results, especialy when handling long documents.
We found that chunkey-bert demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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