Summarization

Jatana

1. Email summarization but with a great intro (see image above)

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

3. Awesome-text-summarization on github

4. Methodical review of abstractive summarization

5. Medium on extractive and abstractive - overview with the abstractive code

6. NAMAS - Neural attention model for abstractive summarization, -Neural Attention Model for Abstractive Sentence Summarization - summarizes single sentences quite well, github

7. Abstractive vs extractive, blue intro

8. Intro to text summarization

9. Paper: survey on text summ, arxiv

10. Very short intro

11. Intro on encoder decoder

12. Unsupervised methods using sentence emebeddings (long and good) - using sent2vec, clustering, picking by rank

13. Abstractive summarization using bert for sota

14. Abstractive

    1. Git1: uses pytorch 0.7, fails to work no matter what i did

    2. Git2, keras code for headlines, missing dataset

    3. Encoder decoder in keras using rnn, claims cherry picked results, the majority is prob not as good

    4. A lot of Text summarization algos on git, using seq2seq, using many methods, glove, etc -

    5. Summarization with point generator networks on git

    6. Summarization based on gigaword claims SOTA

    7. Facebooks neural attention network NAMAS on git

    8. Medium on summarization with tensor flow on news articles from cnn

15. Keywords extraction

    1. The best text rank presentation

    2. Text rank by gensim on medium

    3. Text rank 2

    4. Text rank - custom code, extractive vs abstractive, how to use, some more theoretical info and page rank intuition.

    5. Text rank paper

    6. Improving textrank using adjectival and noun compound modifiers

    7. Unread - New similarity function paper for textrank

16. Extractive summarization

    1. Text rank with glove vectors instead of tf-idf as in the paper (sam)

    2. Medium with code on extractive using word occurrence similarity + cosine, pick top based on rank

    3. Medium on methods, freq, LSA, linking words, sentences,bayesian, graph ranking, hmm, crf,

    4. Wiki on automatic summarization, abstractive vs extractive,

    5. Pyteaser, textteaset, lexrank, pytextrank summarization models & rouge-1/n and blue metrics to determine quality of summarization models Bottom line is that textrank is competitive to sumy_lex

    6. Sumy

    7. Pyteaser

    8. Pytextrank

    9. Lexrank

    10. Gensim tutorial on textrank

    11. Email summarization