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Abstractive Text Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 20
- 보존된 평가 기록
- 98
Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.
Source: Generative Adversarial Network for Abstractive Text Summarization
Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/kh-kim/simple-nmt
https://github.com/khushsi/Finetuning_BART_for_FACET_Summarization
https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks
https://github.com/kiko441500/google_bert
https://github.com/kingcheng2000/bert
https://github.com/knotgrass/attention/blob/main/attn/attention.py
https://github.com/kolloldas/torchnlp
https://github.com/kotu931226/classifier_transformer_pytorch
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/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/ky1994/SpeechRecognition
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/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/llx666/git_demo
https://github.com/longbowking/bert
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.