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Document Summarization
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Automatic Document Summarization is the task of rewriting a document into its shorter form while still retaining its important content. The most popular two paradigms are extractive approaches and abstractive approaches. Extractive approaches generate summaries by extracting parts of the original document (usually sentences), while abstractive methods may generate new words or phrases which are not in the original document.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/buschmo/Simple-German-Corpus
https://github.com/bwallace/RCT-summarization-data
https://github.com/bytedance/neurst
Pay Less Attention with Lightweight and Dynamic Convolutions
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/chesterdu/contrastive_summary
https://github.com/chrisociepa/allamo
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/conceptofmind/LaMDA-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/conceptofmind/lamda-rlhf-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/copenlu/ALPS_2021
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
https://github.com/cosmoquester/2021-dialogue-summary-competition
https://github.com/danchern97/pointer_generator_pytorch
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/daphne12345/SummarizationRadiologyReports
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/denisewong1/ASX300
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
https://github.com/dennlinger/summaries
https://github.com/dqqcasia/st
Pay Less Attention with Lightweight and Dynamic Convolutions
https://github.com/edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018-
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
https://github.com/ezelikman/Context-Is-Everything
https://github.com/facebookresearch/atlas
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/facebookresearch/data2vec_vision
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/facebookresearch/xformers
https://github.com/fuqiang-git-hub/unilmv1-Paddle
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/seqio
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/gulucaptain/dynamictrl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/hkunlp/chunkllama
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/huggingface/swift-coreml-transformers
https://github.com/huggingface/transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ibm/graph_ensemble_learning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ibm/kpa_2021_shared_task
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis
https://github.com/imcaspar/gpt2-ml
https://github.com/ionnoant/DS8008-final-project
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