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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/nakhunchumpolsathien/ThaiSum
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
https://github.com/nguyenphamvan/BertSum-master
https://github.com/no-execution/Summa_label
https://github.com/oceanypt/A-DEEP-REINFORCED-MODEL-FOR-ABSTRACTIVE-SUMMARIZATION
https://github.com/olivia-fsm/p2mcq
https://github.com/openai/gpt-2
https://github.com/pku-yuangroup/open-sora-plan
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/pltrdy/extoracle_summarization
https://github.com/prashanth41/text-summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/pwc-1/Paper-8/tree/main/big_bird
https://github.com/pwc-1/Paper-8/tree/main/bigbird_pegasus
https://github.com/pwc-1/Paper-9/tree/main/longformer
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ramanakshay/nanogpt
https://github.com/raqoon886/KoBertSum
https://github.com/raqoon886/KorBertSum
https://github.com/robinsongh381/unilm_pytorch_korean
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/rohithreddy024/Text-Summarizer-Pytorch
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/rutikasb/abstractive-text-summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/s-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/safakkbilici/Academic-Paper-Title-Recommendation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/sajjjadayobi/ParsBigBird
https://github.com/sanjeevkrn/teaser_generate
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/sblayush/Tensorflow-Attention
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/sblayush/summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/sergeykramp/mthesis-bigbird-embeddings
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/smartschat/tilse
A Temporally Sensitive Submodularity Framework for Timeline Summarization
https://github.com/souvikshanku/translit-former
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/steph1793/Pointer_Generator_Summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/steph1793/Pointer_Transformer_Generator
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/tagoyal/factuality-datasets
https://github.com/tensorflow/models/tree/master/official/nlp/projects/bigbird
https://github.com/thangarani/bertsum
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