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/kpot/keras-transformer
https://github.com/krantirk/BERT-Pretrained-model
https://github.com/kriesbeck/masked-language-prediction
https://github.com/krocki/np-transformer
https://github.com/krystalan/clidsum
https://github.com/krystalan/k-sportssum
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/kukrishna/pointer-generator-pytorch-allennlp
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/kurodenjiro/bert-extractive-summarizer-vietnamese
Leveraging BERT for Extractive Text Summarization on Lectures
https://github.com/ky1994/SpeechRecognition
https://github.com/kylehiroyasu/bert-extractive-summarizer
Leveraging BERT for Extractive Text Summarization on Lectures
https://github.com/kzawisto/unused_information_llm
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/leehyunjun-seoultech/seoultech
https://github.com/leejieun51/transformer
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/lenacabrera/gb_mnmt
https://github.com/lennonzurich/lalala
https://github.com/leshanbog/lasertagger
https://github.com/lesterpjy/numeric-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/libertfan/man
Mask Attention Networks: Rethinking and Strengthen Transformer
https://github.com/lidongliang666/cv_deep_learning
https://github.com/lif31up/attention-is-all-you-need
https://github.com/lif31up/extended-BERT-for-low-rank-adaption
https://github.com/lingyu001/nlp_text_summarization_implementation
https://github.com/liqichen6688/duo-attention
https://github.com/littell/NamedTransformer
https://github.com/lixuanhng/Global-Encoding-for-Abstractive-Summarization
https://github.com/ljyflores/loss-library
Mutual Information Alleviates Hallucinations in Abstractive Summarization
https://github.com/lllyasviel/framepack
Long Document Summarization with Top-down and Bottom-up Inference
https://github.com/llx666/git_demo
https://github.com/longbowking/bert
https://github.com/lovedavidsilva/bert_old_version
https://github.com/lucidrains/DALLE2-pytorch
https://github.com/lucidrains/En-transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.