Text Summarization
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Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.
There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gavincaoyuji/NLP
https://github.com/gengyuanmax/mevtr
https://github.com/gggggeun/DeepLearning_Architecture
https://github.com/giagia96/DeepComedy
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmum/dl-mo-2021
https://github.com/going-doer/paper2code
https://github.com/goldenbili/Bert_Test2
https://github.com/goldenbili/Bert_Test3
https://github.com/goldenbili/bert_lamb_pretrain
https://github.com/goodluck4s/bert-
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/flax/tree/master/examples/wmt
https://github.com/google/seqio
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/t5patches
Controlling Conditional Language Models without Catastrophic Forgetting
https://github.com/googlx/lasertagger
https://github.com/gooofy/zbrain
https://github.com/gooppe/transformer-summarization
https://github.com/gordicaleksa/pytorch-original-transformer
https://github.com/gradjitta/bertsum-streamlit
Leveraging BERT for Extractive Text Summarization on Lectures
https://github.com/graykode/gpt-2-Pytorch
https://github.com/graykode/nlp-tutorial
https://github.com/griff4692/edu-sum
https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network
https://github.com/guinmoon/llmfarm
https://github.com/gulucaptain/dynamictrl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/guluguluhhhh/contrib/tree/43-1/application/factorized-attention-self-attention-with-linear-complexities
https://github.com/guoyaohua/BERT-Chinese-Annotation
https://github.com/guzhang480/Google_BERT
https://github.com/h2oai/h2ogpt
https://github.com/h4ste/oscar
https://github.com/haidershaour/bert
https://github.com/halo090770/bert
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.