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/moon23k/Transformer_Archs
https://github.com/morningmoni/LCGen-eval
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
https://github.com/moussaKam/OrangeSum
BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
https://github.com/mtanghu/LEAP
https://github.com/muazhari/semantic-search
CX DB8: A queryable extractive summarizer and semantic search engine
https://github.com/myamamoto555/tf-bert
https://github.com/mythicalhacker/Text-Summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/nachiketaa/bert
https://github.com/nakhunchumpolsathien/TR-TPBS
https://github.com/nakhunchumpolsathien/ThaiSum
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
https://github.com/nameoverflow/neuro-cangjie
https://github.com/nanzhaogang/contrib/tree/master/application/fastformer-additive-attention-can-b-all-you-need
https://github.com/nargesam/factCC
Evaluating the Factual Consistency of Abstractive Text Summarization
https://github.com/natel9178/transformer-refork
https://github.com/nayeon7lee/bert-summarization
Pretraining-Based Natural Language Generation for Text Summarization
https://github.com/neburseni/NLP
https://github.com/neqkir/attention-mechanism
https://github.com/ngocphucck/BERT-questions-classification
https://github.com/nguyenphamvan/BertSum-master
https://github.com/niccolot/ENG-ITA_NMT
https://github.com/nicholasbao/nlp_job
https://github.com/nietao2/DeepSpeechRecognition
https://github.com/nlinc1905/dsilt-tsa
https://github.com/nlmatics/llmsherpa
https://github.com/nmfisher/bert-modified
https://github.com/no-execution/Summa_label
https://github.com/noriyukipy/tfchat
https://github.com/noriyukipy/tfdlg
https://github.com/nrc-cnrc/sockeye-multisource
https://github.com/nsfzyzz/generalization_metrics_for_nlp
https://github.com/nvshrao/Bidirectional-Transformer
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.