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Document Summarization
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Automatic Document Summarization is the task of rewriting a document into its shorter form while still retaining its important content. The most popular two paradigms are extractive approaches and abstractive approaches. Extractive approaches generate summaries by extracting parts of the original document (usually sentences), while abstractive methods may generate new words or phrases which are not in the original document.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Bread-and-Code/Text-Summarization
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/DanielaStep/SC
Scoring Sentence Singletons and Pairs for Abstractive Summarization
https://github.com/DanielaStep/sentence_similarity_rouge
Scoring Sentence Singletons and Pairs for Abstractive Summarization
https://github.com/EdinburghNLP/XSum
https://github.com/Flo-tyna/Biomedical-Text-Summarizer
https://github.com/HHousen/TransformerSum
https://github.com/Hmkhalla/NLP-summarizer
https://github.com/Iwontbecreative/Abstractive-summarization-OpenNMT
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/JRC1995/Abstractive-Summarization
https://github.com/JunnYu/x-transformers-paddle
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Ki6an/fastT5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KnightZhang625/BERT_TF
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/Kumar-Tarun/summarization
Get To The Point: Summarization with Pointer-Generator Networks
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/LeonZh0u/Chatbot
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/MS-P3/code5/tree/main/gpt2
https://github.com/MS-P3/code5/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/MindSpore-scientific/code-6/tree/main/Neural-Side-by-Side
https://github.com/Nimesh-Patel/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/NirmalenduPrakash/Document-Summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/NirmalenduPrakash/DocumentSummarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/text_summarization/pointer_summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/bigbird
https://github.com/Paleontolog/pointer_summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/Pavyel/Novelty-Detection
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
https://github.com/Rutts07/Summarization-using-Pointer-Generator-Networks
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/Sharif-SLPL/t5-fa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Snoopy666/Extractive-User-Review-Summaruzation-using-LSTM
https://github.com/TidalPaladin/neural-summarizer
https://github.com/TieDanCuihua/Get-To-The-Point-Summarization-with-Pointer-Generator-Networks
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/Ujwal0404/Pointer-Generator-Network
Get To The Point: Summarization with Pointer-Generator Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.