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/JHKim-snu/PGA
https://github.com/JNUpython/bert
https://github.com/JRC1995/Abstractive-Summarization
https://github.com/JRC1995/Machine-Translation-Transformers
https://github.com/JanAthmer/Compositional-generalization-capabillity-of-Transformer
https://github.com/JeheonPark596/sentimentsurvival
https://github.com/JoegameZhou/mPanGu-Alpha-53
https://github.com/JohannLee1996/bert
https://github.com/Joshuaoneheart/Fintech-Final-Report
https://github.com/JudeLee19/HMNet
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
https://github.com/JudeLee19/HMNet-End-to-End-Abstractive-Summarization-for-Meetings
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
https://github.com/JunnYu/x-transformers-paddle
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Jxu-Thu/DIITTO
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Katsumata420/bert_mlm
https://github.com/KeithGalli/pycon2020
https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-
https://github.com/Khalid-Jamal/annotated-transformer-Arabic
https://github.com/Ki6an/fastT5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Kirankumaraswamy/Transformers
https://github.com/KnightZhang625/BERT_TF
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/KrishnaTarun/Deep-Learning-Lab
https://github.com/Kumar-Tarun/summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/KushGrandhi/Polaroid
https://github.com/Kyubyong/transformer
https://github.com/LLluoling/HER
https://github.com/Lemoners/Attention-Is-All-You-Need
https://github.com/LeoLaugier/conditional-auto-encoder-text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/LeoWood/bert-horovod
https://github.com/LeonZh0u/Chatbot
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/LiamMaclean216/Pytorch-Chatbot
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/LoveYang/bert_test
https://github.com/Lsdefine/attention-is-all-you-need-keras
https://github.com/LubdaMax/Data-Science-1
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.