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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/sandeep16064/ats
https://github.com/sanjeevkrn/teaser_generate
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/sarthaxxxxx/Attention-is-all-you-need
https://github.com/satyajitghana/TSAI-DeepNLP-END2.0
https://github.com/saurabhkulkarni77/BERT_multilabel
https://github.com/saurabhnlp/bert
https://github.com/sauram/Machine-Translation-using-Attention-Model
https://github.com/sblayush/Tensorflow-Attention
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/sblayush/summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/scrayish/ML_NLP
https://github.com/semal/bert
https://github.com/semicontinuity/nlp
https://github.com/seominseok0429/inception-I3D-NON-LOCAL
https://github.com/seunghwan1228/Transfomer-MachineTranslation
https://github.com/sf-18/politicalsynthesis
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
https://github.com/sh951011/Attention-Implementation
https://github.com/shahrukhx01/ocr-test
https://github.com/shaikhzhas/bert
https://github.com/shao-chi/ImageCaption
https://github.com/sherinaseri/bert
https://github.com/shibing624/pycorrector
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
https://github.com/shijx12/kqapro_baselines
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/shmsw25/bart-closed-book-qa
https://github.com/shouvikcirca/Transformer
https://github.com/shouvikcirca/Transformer_MiniProject
https://github.com/shreyash8/English_to_Hindi_NMT
https://github.com/shreyashankar/gpt3-sandbox
https://github.com/shuaishuaij/Machine-Translation
https://github.com/shuaizhao95/EDUsum-dataset
https://github.com/shyam-skk/Parallel_CNN_Transfromer
https://github.com/simonjisu/annotated-transformer-kr
https://github.com/sjyttkl/Transformer_learning
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/skt-ai/kobart
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.