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Abstractive Text Summarization
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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/abrazinskas/Copycat-abstractive-opinion-summarizer
Unsupervised Opinion Summarization as Copycat-Review Generation
https://github.com/absummarization/absummarization.github.io
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/adi2103/AML-CoVe
https://github.com/af-ai-center/bert
https://github.com/af-ai-center/nerblackbox
https://github.com/agentdr1/la_mil
https://github.com/ahmetumutdurmus/awd-lstm
https://github.com/ahsanabbas123/Transformer
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/minGPTF
https://github.com/akashe/Python-Code-Generation
https://github.com/algharak/BERTenhance
https://github.com/alibaba-damo-academy/FunASR
https://github.com/alibabasglab/frcrn
https://github.com/alililia/ms_contrib/tree/main/ascend_transformer
https://github.com/alililia/ms_contrib/tree/main/gpu_transformer
https://github.com/alimpiev/embeddings_persistent_homology
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
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/allenai/scientific-claim-generation
https://github.com/amazon-science/chronos-forecasting
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/amazon-science/controllable-readability-summarization
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
https://github.com/amazon-science/fact-graph
Annotating and Modeling Fine-grained Factuality in Summarization
https://github.com/amiyamandal-dev/pegasus
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/amoramine/Pegasus_Longformer_summarization
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/amoramine/Pegasus_with_Longformer_summarization
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/andrejmiscic/simcls-pytorch
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
https://github.com/anhinga/JuliaCon2021-poster
https://github.com/anhtu293/transformer_from_scratch
https://github.com/anirudharc/transformer
https://github.com/anishnarkar/experiments_deeplearning_models
https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.