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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/MarcBS/keras
https://github.com/MashaKubyshina/learning_ML
https://github.com/Matthewdowney18/Transformer_Dialogue
https://github.com/Maz101/Bert
https://github.com/Maz101/pretrainingdataset
https://github.com/MichaelZhouwang/LMlexsub
https://github.com/Mind23-2/MindCode-132
https://github.com/Mind23-2/MindCode-160
https://github.com/MindCode-4/code-10/tree/main/FDDE/train-fine
https://github.com/MindCode-4/code-2/tree/main/pegasus
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/MindCode-4/code-5/tree/main/transformer
https://github.com/MindSpore-paper-code-3/code6/tree/main/CLIFF
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
https://github.com/MindSpore-scientific-2/code-14/tree/main/longt5
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model
https://github.com/MindSpore-scientific/code-12/tree/main/FDDE/train-fine
https://github.com/MindSpore-scientific/code-8/tree/main/Translation-Invariant/model
https://github.com/MirunaPislar/multi-head-attention-labeller
https://github.com/Misoknisky/Bert-MultiGpu
https://github.com/Mleader2/text_scalpel
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/MorenoLaQuatra/bart-it
https://github.com/NVIDIA/sentiment-discovery
https://github.com/Neoanarika/torchexplainer
https://github.com/Nimesh-Patel/BERT
https://github.com/Nimesh-Patel/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/NirmalenduPrakash/Document-Summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/NirmalenduPrakash/DocumentSummarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/Nstats/bert_MRC
https://github.com/Nstats/bert_senti_analysis_ch
https://github.com/Nstats/my_bert
https://github.com/OPAYA/Transformer
https://github.com/Osobarako/alduswarrensewell
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/ernie_gen/modeling.py
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_translation/transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.