Our text summarization solution digests your text collection and builds the crux of the collection through topics, clusters and keywords. Load your text collection from the databases or folders, train them using our NLP models for patterns and unearth the insights as per the modules – Topic Models, Doc Clusters, Keyphrase Highlights, Name Entity Recognition (NER) Graphs. Our text summarization solutions can be implemented on any format of data.
In a Nutshell
Get a bite-sized summary of huge TL; DR documents (invoices, bank statements, investor reports, books, articles, journals, reviews, tweets, comments, legislation). Our text summarization accelerator can analyse local sentiments and global sentiments of our text data.
Functions (Use Cases)
Analyse Voice of Customers to analyse Pain points from reviews from App store, Surveys or Social media platforms.
Identify sentiment and tone of customer to enhance Customer experience.
Identify and extract hidden topics from raw text.
Extract batch number, issue date, email ID, org name etc., irrespective of their position.
Redact confidential information in summaries generated of long TL:DR documents across industries such as legal, finance, healthcare, insurance and more.
Text analytics using Python libraries are used for Summarization and structuring.
Key Phrase Extraction
Python Library: NLTK, re, scikit-learn, Pycrfsuite, Keras
Algorithm: Dependency Parsing, POS based Grammar Chunking, TF-IDF
Python Library: scikit-learn
Algorithm: Non-Negative Matrix Factorization
Python Library: NetworkX, spaCy
Algorithm: Dependency Parsing, Network Analysis
Tagging / Entity Recognition
Python Library: Pycrfsuite, Tensorflow, Keras
Algorithm: Conditional Random Field, LSTM