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Text Summarization

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Our text summarization tool 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 software 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.
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Features

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No manual keywords or topics entry to be extracted

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Customise the extracted output to a XLSX, CSV, JSON, XML file or write to a database

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Understand Local sentiment rather than global sentiment from specific product / service reviews

Schedule a demowith our experts and unlock the potential of your text!

Tech Stack

Text analytics using Python libraries are used for Summarization and structuring.

Key Phrase Extraction

Key Phrase Extraction

Python Library: NLTK, re, scikit-learn, Pycrfsuite, Keras

Algorithm: Dependency Parsing, POS based Grammar Chunking, TF-IDF

Topic Modeling

Topic Modeling

Python Library: scikit-learn

Algorithm: Non-Negative Matrix Factorization

Knowledge Graph

Knowledge Graph

Python Library: NetworkX, spaCy

Algorithm: Dependency Parsing, Network Analysis

Tagging / Entity Recognition

Tagging / Entity Recognition

Python Library: Pycrfsuite, Tensorflow, Keras

Algorithm: Conditional Random Field, LSTM

Quick Links

Demo

See how teX.ai TM can help you glean insights from the text data you possess.

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Brochure

For a quick glance of the advantages teX.ai TM can offer you, download our brochure now!

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FAQs

Our carefully curated FAQs will help address all questions you have about teX.ai TM!

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