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/UdbhavPrasad072300/Transformer-Implementation
https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation
https://github.com/UdbhavPrasad072300/Transformer-Implementations
https://github.com/Ujwal0404/Pointer-Generator-Network
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/VD44/Rouge-L-Tensorflow
https://github.com/VachanVY/gpt.jax
https://github.com/VedantYadav/Transformer
https://github.com/Walter-B/bert-20-classes
https://github.com/WenYanger/General-Transformer-Pytorch
https://github.com/WenjieDu/PyPOTS
https://github.com/Wikidepia/indonesia_dataset
https://github.com/WolfNiu/AdversarialDialogue
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/Wolfie8935/Implementation-of-Attention-is-all-you-need
https://github.com/XINZXZHANG/Bert_repo
https://github.com/XUJitao/WordEmbedding
https://github.com/XapaJIaMnu/transformative
https://github.com/XingxingZhang/dress
https://github.com/Xinsen-Zhang/transformer
https://github.com/XuhanLiu/Transformer
https://github.com/YYGXjpg/BERT_WL
https://github.com/Yipeng91/text_classifier_pub
https://github.com/YuanEric88/bert-nlu
https://github.com/YunwenTechnology/Unilm
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/Zefty/rag-end2end-retriever
https://github.com/Zehui127/SQUAD_BERT
https://github.com/ZmeiGorynych/transformer_pytorch
https://github.com/ZurichNLP/sockeye
https://github.com/a414351664/Bert_MRC
https://github.com/a414351664/my_git_laser
https://github.com/aakash222/text-segmentation-NLP
https://github.com/aananda-giri/gpt2-nepali
https://github.com/aaronH45/sentimentAnalysis
https://github.com/abelriboulot/onnxt5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/abhaskumarsinha/Corpus2GPT
https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.