Papers with Code 아카이브
Abstractive Text Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 20
- 보존된 평가 기록
- 98
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/mindspore-ai/models/tree/master/official/nlp/transformer
https://github.com/mindspore-courses/Deep-Tutorials-for-MindSpore
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/SelfAttention.py
https://github.com/ming024/FastSpeech2
https://github.com/mithunpaul08/bert_tensorflow
https://github.com/mitran27/Attention-is-all-you-Need
https://github.com/moein-shariatnia/Pix2Seq
https://github.com/monimoyd/NLPEnglishToPythonCodeUsingTransformer
https://github.com/monimoyd/NLPMachineTranslationUsingTransformer
https://github.com/moon23k/Scheduled_Sampling
https://github.com/moon23k/Transformer_Anchors
https://github.com/moon23k/Transformer_Archs
https://github.com/morningmoni/LCGen-eval
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
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/nameoverflow/neuro-cangjie
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/noriyukipy/tfchat
https://github.com/noriyukipy/tfdlg
https://github.com/nrc-cnrc/sockeye-multisource
https://github.com/nsfzyzz/generalization_metrics_for_nlp
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.