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.
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https://github.com/lucylow/En_francais_si_vous_plait-
https://github.com/lukemelas/PyTorch-Pretrained-ViT
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/mindspore-nlp-tutorial
https://github.com/lvyufeng/pointer_summarizer_mindspore
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/lyqcom/mbart_mindspore
BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
https://github.com/maanvithag/thinkai
https://github.com/magahub/songrnn
https://github.com/maheshmylavarapu0057/QueryBasedSummarisation
Diversity driven Attention Model for Query-based Abstractive Summarization
https://github.com/majing2019/transformer
https://github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR
https://github.com/manshri/tesum
https://github.com/maria-liakata-nlp-group/wassos
Unsupervised Opinion Summarization as Copycat-Review Generation
https://github.com/maroxtn/tun-sentiment
https://github.com/maxjcohen/transformer
https://github.com/maxmatical/pytorch-projects
https://github.com/maxudong1993/mybert
https://github.com/mayoor/attention_network_experiments
https://github.com/mayurnewase/Quora-Bert
https://github.com/mayurnewase/Translation
https://github.com/megagonlabs/coop
MeanSum: A Neural Model for Unsupervised Multi-document Abstractive Summarization
https://github.com/meghu2791/DeepLearningModels
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
https://github.com/mehdibenamorr/Token-Positional-Bias
https://github.com/mehdibenamorr/bert-positional-bias-ner
https://github.com/meizi1114/bert
https://github.com/mhamedLmarbouh/Attention-is-all-you-need
https://github.com/mhaseeb123/DeepMSim
https://github.com/microcoder-py/attn-is-all-you-need
https://github.com/microsoft/Table-Pretraining
https://github.com/microsoft/ar2
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
https://github.com/microsoft/dialoglm
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
https://github.com/microsoft/fastseq
https://github.com/microsoft/onnxruntime-training-examples
https://github.com/mim-solutions/bert_for_longer_texts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.