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/EncryptedBinary/Meta_analysis
https://github.com/Enigmatisms/Set-Transformer
https://github.com/EthanWYB/bert-classification
https://github.com/EugeneSel/EUMT
https://github.com/EuphoriaYan/bert_component
https://github.com/FaskyCC/TextClassification
https://github.com/FengJiaChunFromSYSU/Bert
https://github.com/Flo-tyna/Biomedical-Text-Summarizer
https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq
https://github.com/Fuchai/language
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold
https://github.com/Ganeshpadmanaban/Neural-Attention-Model
A Neural Attention Model for Abstractive Sentence Summarization
https://github.com/Ganeshpadmanaban/Neural-Attention-Model-Abstractive-Summarization
A Neural Attention Model for Abstractive Sentence Summarization
https://github.com/Gaozhen0816/BERT_QA_For_AILaw
https://github.com/Gaozhen0816/BERT_QA_for_Chinese
https://github.com/Glaceon31/Document-Transformer
https://github.com/GlassyWing/transformer-keras
https://github.com/HHousen/TransformerSum
https://github.com/HKUST-KnowComp/R-Net
https://github.com/HanhaiNotHai/transformer
https://github.com/Harry-Muzart/harry-muzart.github.io
https://github.com/Helsinki-NLP/shared-info
https://github.com/HeshamElAbd/SelfAttentionLangModel
https://github.com/HighCWu/stylegan2-paddle
https://github.com/Hmkhalla/NLP-summarizer
https://github.com/HomebrewNLP/HomebrewNLP-Jax/blob/main/inference.py
https://github.com/HsuWanTing/unified-summarization
A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
https://github.com/HzcIrving/DeepLearning_PlayGround
https://github.com/IBM/pytorch-seq2seq
https://github.com/IOB-Muenster/TEclass2
https://github.com/IrishCoffee/cudnnMultiHeadAttention
https://github.com/Iwontbecreative/Abstractive-summarization-OpenNMT
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/Izecson/saml-nmt
https://github.com/Izecson/sockeye-1.16.6
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.