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

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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