Package: keystem
KeyStemKeyStem 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 foundhere.Table of ContentsAbout the ProjectGetting Started2.1.Installation2.2.Basic Usage2.3.Max Sum Distance2.4.Maximal Marginal Relevance2.5.Embedding Models1. About the ProjectBack to ToCAlthough there 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 whereKeyStemcomes 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.KeyStem 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 apip install keystemand at most 3 lines of code in usage.2. Getting StartedBack to ToC2.1. InstallationInstallation can be done usingpypi:pip install keystem2.2. UsageThe most minimal example can be seen below for the extraction of keywords:fromkeystemimportKeyStemdoc="""Supervised learning is the machine learning task of learning a function thatmaps an input to an output based on example input-output pairs. It infers afunction from labeled training data consisting of a set of training examples.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 thealgorithm to correctly determine the class labels for unseen instances. This requiresthe learning algorithm to generalize from the training data to unseen situations in a'reasonable' way (see inductive bias)."""ks_model=KeyStem()keywords=ks_model.get_keygroups(doc)You can setkeyphrase_ngram_rangeto set the length of the resulting keywords/keyphrases:>>>ks_model.get_keygroups(doc,keyphrase_ngram_range=(1,1),stop_words=None){'index':{0:0,2:1,26:15,28:16,20:11},'keywords':{0:('supervised learning',0.7096),2:('supervised',0.6735),26:('supervised learning',0.613),28:('supervised',0.6125),20:('supervised',0.5554)},'features':{0:'supervised learning',2:'supervised',26:'supervised learning',28:'supervised',20:'supervised'},'cluster':{0:0.0,2:0.0,26:0.0,28:0.0,20:0.0},'score':{0:0.7096,2:0.6735,26:0.613,28:0.6125,20:0.5554},'label':{0:'supervised learning',2:'supervised learning',26:'supervised learning',28:'supervised learning',20:'supervised learning'}To extract keyphrases, simply setkeyphrase_ngram_rangeto (1, 2) or higher depending on the number
of words you would like in the resulting keyphrases:>>>kw_model.extract_keywords(doc,keyphrase_ngram_range=(1,2),stop_words=None){'index':{0:0,2:1,26:15,28:16,20:11},'keywords':{0:('supervised learning',0.7096),2:('supervised',0.6735),26:('supervised learning',0.613),28:('supervised',0.6125),20:('supervised',0.5554)},'features':{0:'supervised learning',2:'supervised',26:'supervised learning',28:'supervised',20:'supervised'},'cluster':{0:0.0,2:0.0,26:0.0,28:0.0,20:0.0},'score':{0:0.7096,2:0.6735,26:0.613,28:0.6125,20:0.5554},'label':{0:'supervised learning',2:'supervised learning',26:'supervised learning',28:'supervised learning',20:'supervised learning'}2.4. Maximal Marginal RelevanceTo diversify the results, we can use Maximal Margin Relevance (MMR) to create
keywords / keyphrases which is also based on cosine similarity. The results
withhigh diversity:The results withlow diversity:2.5. Embedding ModelsKeyBERT supports many embedding models that can be used to embed the documents and words:Sentence-TransformersFlairSpacyGensimUSEClickherefor a full overview of all supported embedding models.Sentence-TransformersYou can select any model fromsentence-transformershereand pass it through KeyStem withmodel:fromkeystemimportKeyStemkw_model=KeyStem(model='all-MiniLM-L6-v2')Or select a SentenceTransformer model with your own parameters:fromkeystemimportKeyStemfromsentence_transformersimportSentenceTransformersentence_model=SentenceTransformer("all-MiniLM-L6-v2")kw_model=KeyStem(model=sentence_model)FlairFlairallows you to choose almost any embedding model that
is publicly available. Flair can be used as follows:fromkeystemimportKeyStemfromflair.embeddingsimportTransformerDocumentEmbeddingsroberta=TransformerDocumentEmbeddings('roberta-base')ks_model=KeyStem(model=roberta)You can select any 🤗 transformers modelhere.CitationTo cite KeyStem in your work, please use the following bibtex reference:@misc{grootendorst2020keybert,author={Naga Kiran},title={KeyStem: Minimal keyword extraction with BERT and grouping to the stem of key.},year=2023,publisher={caspai},version={v0.0.1},url={http://caspai.in/}}ReferencesBelow, you can find several resources that were used for the creation of KeyStem
but most importantly, these are amazing resources for creating impressive keyword extraction models:Github Repos:https://github.com/MaartenGr/KeyBERThttps://github.com/Nagakiran1/keystemhttps://github.com/thunlp/BERT-KPEhttps://github.com/ibatra/BERT-Keyword-Extractorhttps://github.com/pranav-ust/BERT-keyphrase-extractionhttps://github.com/swisscom/ai-research-keyphrase-extractionMMR:
The selection of keywords/keyphrases was modeled after:https://github.com/swisscom/ai-research-keyphrase-extraction