keybert 0.1.1

KeyBERT performs keyword extraction with state-of-the-art transformer models.

KeyBERT

KeyBERT is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document.

Corresponding medium post can be found here.

Table of Contents

1. About the Project
2. Getting Started
   2.1. Installation
   2.2. Basic Usage

1. About the Project

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Although that are already many methods available for keyword generation (e.g., Rake, YAKE!, TF-IDF, etc.) I wanted to create a very basic, but powerful method for extracting keywords and keyphrases. This is where KeyBERT comes in! Which uses BERT-embeddings and simple cosine similarity to find the sub-phrases in a document that are the most similar to the document itself.

First, document embeddings are extracted with BERT to get a document-level representation. Then, word embeddings are extracted for N-gram words/phrases. Finally, we use cosine similarity to find the words/phrases that are the most similar to the document. The most similar words could then be identified as the words that best describe the entire document.

KeyBERT is by no means unique and is created as a quick and easy method for creating keywords and keyphrases. Although there are many great papers and solutions out there that use BERT-embeddings (e.g., 1, 2, 3, ), I could not find a BERT-based solution that did not have to be trained from scratch and could be used for beginners (correct me if I'm wrong!). Thus, the goal was a pip install keybert and at most 3 lines of code in usage.

NOTE: If you use MMR to select the candidates instead of simple cosine similarity, this repo is essentially a simplified implementation of EmbedRank with BERT-embeddings.

2. Getting Started

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2.1. Installation

PyTorch 1.2.0 or higher is recommended. If the install below gives an error, please install pytorch first here.

Installation can be done using pypi:

pip install keybert

2.2. Usage

The most minimal example can be seen below for the extraction of keywords:

from keybert import KeyBERT

doc = """
         Supervised learning is the machine learning task of learning a function that
         maps an input to an output based on example input-output pairs.[1] It infers a
         function from labeled training data consisting of a set of training examples.[2]
         In supervised learning, each example is a pair consisting of an input object
         (typically a vector) and a desired output value (also called the supervisory signal). 
         A supervised learning algorithm analyzes the training data and produces an inferred function, 
         which can be used for mapping new examples. An optimal scenario will allow for the 
         algorithm to correctly determine the class labels for unseen instances. This requires 
         the learning algorithm to generalize from the training data to unseen situations in a 
         'reasonable' way (see inductive bias).
      """
model = KeyBERT('distilbert-base-nli-mean-tokens')
keywords = model.extract_keywords(doc)

You can set keyphrase_length to set the length of the resulting keyphras:

>>> model.extract_keywords(doc, keyphrase_length=1, stop_words=None)
['learning', 
 'training', 
 'algorithm', 
 'class', 
 'mapping']

To extract keyphrases, simply set keyphrase_length to 2 or higher depending on the number of words you would like in the resulting keyphrases:

>>> model.extract_keywords(doc, keyphrase_length=3, stop_words=None)
['learning algorithm',
 'learning machine',
 'machine learning',
 'supervised learning',
 'learning function']

To diversify the results, we can use Maximal Margin Relevance (MMR) to create keywords / keyphrases which is also based on cosine similarity. The results with high diversity:

>>> model.extract_keywords(doc, keyphrase_length=3, stop_words='english', use_mmr=True, diversity=0.7)
['algorithm generalize training',
 'labels unseen instances',
 'new examples optimal',
 'determine class labels',
 'supervised learning algorithm']

The results with low diversity:

>>> model.extract_keywords(doc, keyphrase_length=3, stop_words='english', use_mmr=True, diversity=0.2)
['algorithm generalize training',
 'learning machine learning',
 'learning algorithm analyzes',
 'supervised learning algorithm',
 'algorithm analyzes training']

References

Below, you can find several resources that were used for the creation of KeyBERT but most importantly, are amazing resources for creating impressive keyword extraction models:

Papers:

• Sharma, P., & Li, Y. (2019). Self-Supervised Contextual Keyword and Keyphrase Retrieval with Self-Labelling.

Github Repos:

• https://github.com/thunlp/BERT-KPE
• https://github.com/ibatra/BERT-Keyword-Extractor
• https://github.com/pranav-ust/BERT-keyphrase-extraction
• https://github.com/swisscom/ai-research-keyphrase-extraction

MMR:
The selection of keywords/keyphrases was modelled after:

• https://github.com/swisscom/ai-research-keyphrase-extraction

NOTE: If you find a paper or github repo that has an easy-to-use implementation of BERT-embeddings for keyword/keyphrase extraction, let me know! I'll make sure to add it a reference to this repo.