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  1. Natural Language Processing

Summarization

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Last updated 2 years ago

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Jatana
  1. - words assigned weighted frequency, summed up in sentences and then selected based on the top K scored sentences.

  2. - , - - summarizes single sentences quite well,

  3. ,

  4. - using sent2vec, clustering, picking by rank

  5. Abstractive

    1. on git

    2. NAMAS on git

  6. Keywords extraction

    1. Unread -

  7. Extractive summarization

    1. (sam)

    2. Bottom line is that textrank is competitive to sumy_lex

Email summarization but with a great intro (see image above)
With nltk
Awesome-text-summarization on github
Methodical review of abstractive summarization
Medium on extractive and abstractive - overview with the abstractive code
NAMAS
Neural attention model for abstractive summarization
Neural Attention Model for Abstractive Sentence Summarization
github
Abstractive vs extractive, blue intro
Intro to text summarization
Paper: survey on text summ
arxiv
Very short intro
Intro on encoder decoder
Unsupervised methods using sentence emebeddings (long and good)
Abstractive summarization using bert for sota
Git1: uses pytorch 0.7, fails to work no matter what i did
Git2, keras code for headlines, missing dataset
Encoder decoder in keras using rnn, claims cherry picked results, the majority is prob not as good
A lot of Text summarization algos on git, using seq2seq, using many methods, glove, etc -
Summarization with point generator networks
Summarization based on gigaword claims SOTA
Facebooks neural attention network
Medium on summarization with tensor flow on news articles from cnn
The best text rank presentation
Text rank by gensim on medium
Text rank 2
Text rank - custom code, extractive vs abstractive, how to use, some more theoretical info and page rank intuition.
Text rank paper
Improving textrank using adjectival and noun compound modifiers
New similarity function paper for textrank
Text rank with glove vectors instead of tf-idf as in the paper
Medium with code on extractive using word occurrence similarity + cosine, pick top based on rank
Medium on methods, freq, LSA, linking words, sentences,bayesian, graph ranking, hmm, crf,
Wiki on automatic summarization, abstractive vs extractive,
Pyteaser, textteaset, lexrank, pytextrank summarization models & rouge-1/n and blue metrics to determine quality of summarization models
Sumy
Pyteaser
Pytextrank
Lexrank
Gensim tutorial on textrank
Email summarization