Text Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 12
- 평가표
- 37
- 보존된 평가 기록
- 233
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/allenai/open-mds
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/allenai/scientific-claim-generation
https://github.com/allisontam/bills867
BillSum: A Corpus for Automatic Summarization of US Legislation
https://github.com/amagooda/SummaRuNNer_coattention
https://github.com/amazon-science/chronos-forecasting
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/amazon-science/fact-graph
Annotating and Modeling Fine-grained Factuality in Summarization
https://github.com/amiyamandal-dev/pegasus
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/amoramine/Pegasus_Longformer_summarization
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/amoramine/Pegasus_with_Longformer_summarization
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/anbunathan/WikiHow-Semantic
https://github.com/andrejmiscic/simcls-pytorch
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
https://github.com/anhinga/JuliaCon2021-poster
https://github.com/anhtu293/transformer_from_scratch
https://github.com/anirudharc/transformer
https://github.com/anishnarkar/experiments_deeplearning_models
https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION
https://github.com/antoinecollas/transformer_neural_machine_translation
https://github.com/anton-bushuiev/ppiformer
https://github.com/anuprulez/clade_prediction
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization
https://github.com/anuscode/nrms
https://github.com/apexmeister/PGN_pytorch_lightning
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/appcoreopc/berty
https://github.com/arkel23/PyTorch-Pretrained-ViT
https://github.com/arnelochner/GBTBMDS
Leveraging Graph to Improve Abstractive Multi-Document Summarization
https://github.com/asahi417/lm-question-generation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/asahi417/lmppl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/aseidelo/wiki_generator
https://github.com/asfathermou/human-computer-interaction
https://github.com/asjad99/data_products
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
https://github.com/asultan123/DL_LLC_Prefetching
https://github.com/augustgw/early-exit-transformer
https://github.com/austnbell/TextSummarizationFederatedLearning
https://github.com/autobotasia/vibert
https://github.com/awalther/scibart
https://github.com/awslabs/sockeye
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.