https://github.com/smartschat/tilse
A Temporally Sensitive Submodularity Framework for Timeline Summarization
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/socc-io/piqaboo
https://github.com/sonalisrijan/Deep-Learning-based-Defect-Prediction-Model-for-Source-Code
https://github.com/sonrisa07/transformer
https://github.com/sooftware/Attention-Implementation
https://github.com/sooftware/attentions
https://github.com/sooftware/nlp-attentions
https://github.com/soumik12345/transformer.pytorch
https://github.com/souvikshanku/translit-former
https://github.com/steph1793/Pointer_Generator_Summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/steph1793/Pointer_Transformer_Generator
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/stevinc/Transformer_Timeseries
https://github.com/studio-ousia/luke
https://github.com/summarization/semantic_summ
Toward Abstractive Summarization Using Semantic Representations
https://github.com/svakulenk0/response_eval
https://github.com/swimmingCreative/DeepSpeechRecognition-master
https://github.com/t080/pytorch-translm
Sample Efficient Text Summarization Using a Single Pre-Trained Transformer
https://github.com/tangg555/sabart
https://github.com/tanjeffreyz/attention-is-all-you-need
https://github.com/tatp22/linformer-pytorch
https://github.com/tatp22/multidim-positional-encoding
https://github.com/tbmoon/LANL_Earthquake_Prediction
https://github.com/tcnguyen/bert
https://github.com/tcxdgit/MachineTranslation_t2t
https://github.com/tensorops/TransformerX
https://github.com/text-machine-lab/transformerpy
https://github.com/thangarani/bertsum
https://github.com/thanhlong1997/bert_quora
https://github.com/the-Quert/iNLPfun
https://github.com/theQuert/inlpfun
https://github.com/thecodemasterk/BERT
https://github.com/thecodemasterk/Text-to-Text-transfer-transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thinkwee/SubjectiveBiasABS
https://github.com/thomasscialom/summa-qa
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models
https://github.com/threelittlemonkeys/pointer-network-pytorch
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/thu-keg/omnievent
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.