https://github.com/ishtoo1/Text-Summarization-using-Attention
Get To The Point: Summarization with Pointer-Generator Networks
Papers with Code 아카이브
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/ishtoo1/Text-Summarization-using-Pointer-Generator-Model
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/itzprashu1/prashant
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/j40903272/bottom-up-summary
https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/jananiarunachalam/Research-Paper-Summarization
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jiaruncao/BioCopyMechanism
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/jiminsun/pointer-generator
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/johnnyb1509/2020_VBDI_DL
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/junnyu/paddle_t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jzhang38/longmamba
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/kedz/nnsum
https://github.com/kotyukov/huaweiNLP
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/lesterpjy/numeric-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/lingyu001/nlp_text_summarization_implementation
https://github.com/lucidrains/marge-pytorch
https://github.com/lucidrains/memory-compressed-attention
https://github.com/lululuyi/longheads
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/luomancs/retriever_reader_for_okvqa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/lvyufeng/bert4ms
https://github.com/lvyufeng/pointer_summarizer_mindspore
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/magic282/NeuSum
Neural Document Summarization by Jointly Learning to Score and Select Sentences
https://github.com/manavkapadnis/enigma_argmining
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis
https://github.com/manshri/tesum
https://github.com/milmor/GPT
https://github.com/mim-solutions/bert_for_longer_texts
https://github.com/mim-solutions/roberta_for_longer_texts
https://github.com/mindspore-ai/models/blob/master/research/nlp/gpt2
https://github.com/minimaxir/gpt-2-simple
https://github.com/monologg/kobigbird
https://github.com/mythicalhacker/Text-Summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/nachotp/BertCommentSum
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.