chunkey-bert 0.2.0

Modification of the KeyBERT method to extract keywords and keyphrases using chunks. This provides better results, especialy when handling long documents.

ChunkeyBERT

Overview

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.

Installation

Install from PyPI using pip (preferred method):

pip install chunkey-bert

Experimental results

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/.

Contribution and feedback

Contributions and feedback are most welcome. Please see CONTRIBUTING.md for further details.