Text Summarization
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Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.
There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Santosh-Gupta/Arxiv-Manatee
https://github.com/Satan012/BERT
https://github.com/SeoroMin/transformer_pytorch
https://github.com/SeoroMin/transformer_pytorch_ver2
https://github.com/SergiBragos/DLAI-2021-project
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/Sharif-SLPL/t5-fa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Sharpiless/Versailles-text-generation-with-paddlepaddle
https://github.com/Shasvat-Desai/Shasvat-Desai
https://github.com/Shinya-Kouda/kgc
https://github.com/ShivamRajSharma/Transformer-Architectures-From-Scratch
https://github.com/ShreyasArthur/StyleGAN-2-with-Urban-Plans
https://github.com/ShuvenduBikash/transformer_spelling_corrector
https://github.com/ShwetaBaranwal/Transformer
https://github.com/SiavashCS/sgan_simple
https://github.com/Skumarr53/Attention-is-All-you-Need-PyTorch
https://github.com/Snoopy666/Extractive-User-Review-Summaruzation-using-LSTM
https://github.com/SpikeKing/My-Bert
https://github.com/StevenLai1994/AM_and_LM
https://github.com/StillKeepTry/Transformer-PyTorch
https://github.com/StoneGH/bert
https://github.com/THUNLP-MT/THUMT
https://github.com/TSLNIHAOGIT/bert
https://github.com/TSLNIHAOGIT/bert_run
https://github.com/TYTYTYTYTYTYTYTYTY/558-project
https://github.com/TaiseiAso/BiGruAttEncDec
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https://github.com/TellinaTool/nl2bash
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
https://github.com/The-AI-Summer/self_attention
https://github.com/TheTensorDude/transformer-tf
https://github.com/ThierryBarros/reproduction-textrank
https://github.com/TidalPaladin/neural-summarizer
https://github.com/TieDanCuihua/Get-To-The-Point-Summarization-with-Pointer-Generator-Networks
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/TieDanCuihua/transformer-Attention-is-All-You-Need
https://github.com/Tixierae/OrangeSum
BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
https://github.com/Tixierae/gow_tools
https://github.com/TonyX19/bert_hyperpartisan
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.