An unsupervised text summarization and information retrieval library under the hood using natural language processing models.
A simple to use yet robust python library containing tools to perform:
- Text summarization
- Information retrieval
- Finding similarities
- Sentence ranking
- Keyword extraction
- and many more in progress...
These instructions will get you a copy of the project and ready for use for your python projects.
Download from PyPi.org
pip install pynutshell
Requires Python version >=3.6
Clone this repository using the command:
git clone https://github.com/KrishnanSG/Nutshell.git cd Nutshell
Then install the library using the command:
python setup.py install
Note: The package is distributed as pynutshell due to unavailability of the name, but the package name is nutshell and request you not to get confused.
How does the library work?
The library has several components:
- Similarity Algorithms
- Information Retrievers
- Keyword Extractors
A technique of transforming or condensing textual information using natural language processing techniques.
Types of summarization
This technique is very much similar to highlighting important sentence while we read a book.
The algorithm finds the important sentences in the corpus (NLP term for raw input text) by reducing the similarity between sentence by removing sentences which are very similar to each other by retaining one among them.
Though this method is a powerful it fails to combine 2 or more sentences into a complex sentence, there by not provide optimal result for some cases.
This technique unlike extractive is much more complex and robust in producing summaries. The algorithm used for this technique performs sentence clustering using Semantic Analysis (finding the meaning of sentence).
Text rankers are algorithms similar to web page ranking algorithms used to rank web pages. These rankers find the importance of the sentence in the document and provide ranks to the sentence, thereby providing us with the information of how important the sentence is.
Text similarity algorithms define the similarity between 2 documents (sentences).
A few classic algorithms for finding similarity are:
- Cosine Similarity
- Euclidean Distance
Note: word2vec is an important transformation step used to convert words into vectors to easily perform mathematical operations.
Checklist of features the library currently offers and plans to offer.
- Keyword Extraction
- Text Tokenizers
- Text cleaners
- Semantic decoder
- Text Rankers
- Information Retrieval
A simple example on how to use the library and perform extractive text summarization from the given input text(corpus).
from nutshell.algorithms.information_retrieval import ClassicalIR from nutshell.algorithms.ranking import TextRank from nutshell.algorithms.similarity import BM25Plus from nutshell.model import Summarizer from nutshell.preprocessing.cleaner import NLTKCleaner from nutshell.preprocessing.preprocessor import TextPreProcessor from nutshell.preprocessing.tokenizer import NLTKTokenizer from nutshell.utils import load_corpus, construct_sentences_from_ranking # Example corpus = load_corpus('input.txt') print("\n --- Original Text ---\n") print(corpus) preprocessor = TextPreProcessor(NLTKTokenizer(), NLTKCleaner()) similarity_algorithm = BM25Plus() ranker = TextRank() ir = ClassicalIR() # Text Summarization model = Summarizer(preprocessor, similarity_algorithm, ranker, ir) summarised_content = model.summarise(corpus, reduction_ratio=0.70, preserve_order=True) print("\n --- Summarized Text ---\n") print(construct_sentences_from_ranking(summarised_content))
A simple example on how to use the library and perform keyword extraction from the given input text(corpus).
from nutshell.algorithms.information_retrieval import ClassicalIR from nutshell.model import KeywordExtractor from nutshell.preprocessing.cleaner import NLTKCleaner from nutshell.preprocessing.preprocessor import TextPreProcessor from nutshell.preprocessing.tokenizer import NLTKTokenizer from nutshell.utils import load_corpus corpus = load_corpus('input.txt') print("\n --- Original Text ---\n") print(corpus) # Text Keyword Extraction preprocessor = TextPreProcessor(NLTKTokenizer(), NLTKCleaner(skip_stemming=True)) keyword_extractor = KeywordExtractor(preprocessor, ClassicalIR()) keywords = keyword_extractor.extract_keywords(corpus, count=10, raw=False) print("\n --- Keywords ---\n") print(keywords)
Contributions are always welcomed, it would be great to have people use and contribute to this project to help user understand and benefit from library.
How to contribute
- Create an issue: If you have a new feature in mind, feel free to open an issue and add some short description on what that feature could be.
- Create a PR: If you have a bug fix, enhancement or new feature addition, create a Pull Request and the maintainers of the repo, would review and merge them.
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