Papers with Code 아카이브
Abstractive Text Summarization
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- 하위 태스크
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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/yahah100/text_summarization
Pretraining-Based Natural Language Generation for Text Summarization
https://github.com/yangyucheng000/University/tree/main/model-1/pegasus
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/yangyucheng000/University/tree/main/model-1/pegasus_x
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/yanqi1811/attention-is-all-you-need
https://github.com/yaushian/Unparalleled-Text-Summarization-using-GAN
Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks
https://github.com/yeeeqichen/Bert
https://github.com/yinghanlong/SRtransformer
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
https://github.com/yingtaomj/Learning-towards-Abstractive-Timeline-Summarization
https://github.com/yiyc-kor/bert-study
https://github.com/yizhongw/tk-instruct
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ykrmm/ICLR_2020
https://github.com/ykrmm/TREMBA
https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/yoongtr/Grammar-Error-Correction-with-OpenNMT-tf
https://github.com/ypeleg/MinimalIsAllYouNeed
https://github.com/yuanmu97/secure-transformer-inference
https://github.com/yueyongjiao/Transformer-Contextual
https://github.com/yueyongjiao/attention_is_all_you_need_pytorch
https://github.com/yuhangT/tf_bert
https://github.com/yumoh/speech-keras
https://github.com/yunzhusong/AAAI20-PORLHG
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
https://github.com/yydai/bert_test
https://github.com/zankner/Transformer
https://github.com/zapplea/bert
https://github.com/zbp-xxxp/R-Drop-Paddle
https://github.com/zer0sh0t/artificial_intelligence/tree/master/transformer
https://github.com/zhang-huihui/git-repository
https://github.com/zhangbo2008/best_pytorch_transformer
https://github.com/zhdbwe/Paper-DailyReading
https://github.com/zhiqic/chartreader
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/zide05/pointer-gen-fastnlp
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/zipeiyang/DeepSpeechRecognition
https://github.com/zpc-666/Paddle-R-Drop
https://github.com/zsweet/BERT_zsw
https://github.com/zysite/post
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.