A simple python implementation of the Maximal Marginal Relevance (MMR) baseline system for text summarization.
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Updated
Jan 20, 2017 - Python
A simple python implementation of the Maximal Marginal Relevance (MMR) baseline system for text summarization.
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.
Generating graphical visualization of e-books which gives best explained section of the books in terms of centrality and relevance
LexRank and MMR package for Japanese documents
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.
Text Summarization using LSTM_Attention, TextRank,PyTextRank, LexRank, Gensim and PyTeaser
Automated text summarization system using Lexical chains and Lex Rank.
This Python code scrapes Google search results then applies sentiment analysis, generates text summaries, and ranks keywords.
An automated Text summarizer & Essay grading model was built using Natural Language Processing (NLP) which was then deployed using Flask in Python.
Проект по курсу Физтеха "Методы оптимизации". Суть проекта заключается в исследовании методов extractive summarization.
Unsupervised text summarization using the lexrank algorithm
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