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/openaccess-ai-collective/axolotl
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
https://github.com/openai/gpt-2
https://github.com/opennmt/ctranslate2
https://github.com/orhonovich/q-squared
https://github.com/overwindows/PALM
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
https://github.com/owainwest/uniprot_bert
https://github.com/paolanu/BERT_epitope
https://github.com/parcorelab/comscribe
https://github.com/park-cheol/ASR-Conformer
https://github.com/patrickhart/jaxdl
https://github.com/paulskeie/stadnamn
https://github.com/pengming617/bert_classification
https://github.com/pengshuyuan/Bert
https://github.com/penny9287/sockeye
https://github.com/pennylaneai/generative-quantum-states
https://github.com/phohenecker/pytorch-transformer
https://github.com/pierrecolombo/nlg_eval_via_simi_measures
Automatic Text Evaluation through the Lens of Wasserstein Barycenters
https://github.com/pingheng001/Cnn-Bert
https://github.com/piquil/rydberggpt
https://github.com/pku-yuangroup/open-sora-plan
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/plkmo/Transformer-Eng2French
https://github.com/pltrdy/extoracle_summarization
https://github.com/pltrdy/public_meetings
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation
https://github.com/poethan/cushLEPOR
Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods
https://github.com/pomonam/AttentionCluster
https://github.com/prabhupad26/100daysofML
https://github.com/prashanth41/text-summarization
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/praveenjune17/BERT_text_summarisation
Pretraining-Based Natural Language Generation for Text Summarization
https://github.com/pritam1322/Machine-Translation-with-Transformer
https://github.com/priyamtejaswin/multistep-retrieve-summarize
https://github.com/pvl/wikihow_pairs_dataset
https://github.com/pwc-1/Paper-8/tree/main/big_bird
https://github.com/pwc-1/Paper-8/tree/main/bigbird_pegasus
https://github.com/pwc-1/Paper-9/tree/main/1/xlm_prophetnet
https://github.com/pwc-1/Paper-9/tree/main/2/mt5
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.