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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/VachanVY/gpt.jax
https://github.com/W4ngatang/qags
https://github.com/WolfNiu/AdversarialDialogue
Get To The Point: Summarization with Pointer-Generator Networks
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/aananda-giri/gpt2-nepali
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/MinimalGPT
https://github.com/abisee/cnn-dailymail
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/abstatic/ire_project18
https://github.com/adrian9631/TextSumma
https://github.com/affjljoo3581/GPT2
https://github.com/aikawasho/BertSum
https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/akanyaani/gpt-2-tensorflow2.0
https://github.com/akanyaani/minGPTF
https://github.com/alebryvas/berk266
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/mslr-shared-task
https://github.com/allenai/open-mds
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
https://github.com/amagooda/SummaRuNNer_coattention
https://github.com/amazon-science/chronos-forecasting
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/apexmeister/PGN_pytorch_lightning
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/arnelochner/GBTBMDS
Leveraging Graph to Improve Abstractive Multi-Document Summarization
https://github.com/asahi417/lm-question-generation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/asahi417/lmppl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/aseidelo/wiki_generator
https://github.com/asjad99/data_products
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
https://github.com/atulkum/pointer_summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/austnbell/TextSummarizationFederatedLearning
https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/becxer/pointer-generator
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/bigscience-workshop/architecture-objective
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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