LexRank algorithm for text summarization
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Updated
Apr 14, 2024 - Python
LexRank algorithm for text summarization
A simple python implementation of the Maximal Marginal Relevance (MMR) baseline system for text summarization.
This Python code scrapes Google search results then applies sentiment analysis, generates text summaries, and ranks keywords.
This Python code retrieves thousands of tweets, classifies them using TextBlob and VADER in tandem, summarizes each classification using LexRank, Luhn, LSA, and LSA with stopwords, and then ranks stopwords-scrubbed keywords per classification.
Generating graphical visualization of e-books which gives best explained section of the books in terms of centrality and relevance
LexRank for ranking documents containing some keyword or keyphrase using cosine similarities of either naive, tfidf, or idf-modified-cosine. Non-query ranking also supported.
Research on enhancing the LexRank-based text summarization system by incorporating semantic similarity measures from the ECNU system
Implementation of various Extractive Text Summarization algorithms.
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