Text Summarization
To summarize a large amount of text content into a short & precise summary using NLP
To process the content from a text dumped as input or data scrapped from a website link
Features
Content Summarizer wraps up long text into main points so that we can read any long text in short form. The features considered include sentence length cut-off and finding thematic word
- Language Proficiency: English, German and French
- Choosing the Content Type: Generic, Review, Complaint, Feedback
- Input text paragraph or Link
- Word Count for input and processed data
- Estimated Average Reading Time to read the processed text
- Word Cloud representing the important features from the text
Model Implementation
Model 1
SpaCy
Offers tokenization, sentence boundary detection, POS tagging, syntactic parsing, integrated word vectors, and alignment into the original
Model 2
LexRank
It is an unsupervised graph based approach for automatic text summarization. The scoring of sentences is done using the graph method.
Model 3
Gensim
Designed to automatically extract semantic topics from documents. It is based on ranks of text sentences using a variation of the TextRank algorithm.
Model 4
NLTK
It provides resources like WordNet, text processing libraries for classification, tokenization, stemming, tagging, parsing & semantic reasoning, wrappers.
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