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/pwc-1/Paper-9/tree/main/longformer
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
https://github.com/pwc-1/Paper-9/tree/main/longt5
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/qywu/memformers
https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch
https://github.com/rainyBJ/Fastformer-Paddle
https://github.com/rajeev595/RHS_HierNSE
Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach
https://github.com/rajlm10/Chandler
https://github.com/ramanakshay/nanogpt
https://github.com/ramanakshay/transformer
https://github.com/raphael-sch/SumVAE
Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder
https://github.com/raqoon886/KoBertSum
https://github.com/raqoon886/KorBertSum
https://github.com/rasbot/plot-bot
https://github.com/raufer/bert-summarization
Pretraining-Based Natural Language Generation for Text Summarization
https://github.com/raymin0223/fast_robust_early_exit
https://github.com/re-search/DocProduct
https://github.com/reactive-systems/circuit-repair
https://github.com/reactive-systems/ml2
https://github.com/reallygooday/60daysofudacity
https://github.com/redevaaa/Transformer-for-EEG
https://github.com/reppy4620/Dialog
https://github.com/ricardordb/bert
https://github.com/richard-peng-xia/Chinese-Noisy-Text
https://github.com/richard88888/chinese-noisy-text
https://github.com/rickyHong/Google-BERT-repl
https://github.com/ricsinaruto/NeuralChatbots-DataFiltering
https://github.com/ricsinaruto/dialog-eval
https://github.com/rishavbhurtel/sentiment-classifier
https://github.com/rizwan09/paper
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
https://github.com/robinsongh381/unilm_pytorch_korean
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/rohan400/PepperQA
https://github.com/rohithreddy024/Text-Summarizer-Pytorch
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/rojagtap/abstractive_summarizer
https://github.com/rpuiggari/bert2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.