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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/oceanypt/A-DEEP-REINFORCED-MODEL-FOR-ABSTRACTIVE-SUMMARIZATION
https://github.com/okkteam/Transformer-Transducer
https://github.com/olivia-fsm/p2mcq
https://github.com/ololo123321/maestro
https://github.com/open-mmlab/mmclassification
https://github.com/opennmt/ctranslate2
https://github.com/orhonovich/q-squared
https://github.com/overwindows/PALM
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
https://github.com/owainwest/uniprot_bert
https://github.com/paolanu/BERT_epitope
https://github.com/parcorelab/comscribe
https://github.com/park-cheol/ASR-Conformer
https://github.com/patrickhart/jaxdl
https://github.com/paulskeie/stadnamn
https://github.com/pengming617/bert_classification
https://github.com/pengshuyuan/Bert
https://github.com/penny9287/sockeye
https://github.com/pennylaneai/generative-quantum-states
https://github.com/phohenecker/pytorch-transformer
https://github.com/pingheng001/Cnn-Bert
https://github.com/piquil/rydberggpt
https://github.com/plkmo/Transformer-Eng2French
https://github.com/pomonam/AttentionCluster
https://github.com/prabhupad26/100daysofML
https://github.com/prashanth41/text-summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/praveenjune17/BERT_text_summarisation
Pretraining-Based Natural Language Generation for Text Summarization
https://github.com/pritam1322/Machine-Translation-with-Transformer
https://github.com/priyamtejaswin/multistep-retrieve-summarize
https://github.com/pwc-1/Paper-9/tree/main/2/mt5
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
https://github.com/pwc-1/Paper-9/tree/main/4/Translation-Invariant/model
https://github.com/pwc-1/Paper-9/tree/main/longformer
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
https://github.com/pwc-1/Paper-9/tree/main/longt5
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/qywu/memformers
https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.