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/vsubramaniam851/typ_embed
https://github.com/vulus98/rethinking-attention
https://github.com/vvvm23/TchAIkovsky-Legacy
https://github.com/wadoodabdul/clinical_ner_benchmark
https://github.com/walkacross/transformer-pytorch
https://github.com/wangclnlp/deepspeed-chat-extension
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
https://github.com/wangcongcong123/ttt
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/wangmz15/Chinese-Error-Correction-with-THUMT
https://github.com/wayalhruhi/gogle_bert
https://github.com/wchh127/yykf
https://github.com/weipingfu/qgeval
https://github.com/weiwang2330/sparse-structured-attention
A Regularized Framework for Sparse and Structured Neural Attention
https://github.com/whr94621/NJUNMT-pytorch
https://github.com/why2000/DuReader-bert
https://github.com/wikidepia/indonesian_datasets
https://github.com/wilile26811249/Fastformer-PyTorch
https://github.com/wuhao050698/Abstractive-Summarization
https://github.com/wxt1997/Transformer-Transducer
https://github.com/wyu97/Easy-use-BART
https://github.com/wyu97/permgen
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/wzh326/R-Drop
https://github.com/wzlxjtu/PositionalEncoding2D
https://github.com/xbtlin/All-about-Machine-Learning
https://github.com/xcfcode/Summarization-Papers
https://github.com/xcfcode/msamsum
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
https://github.com/xcmyz/FastSpeech
https://github.com/xesdiny/test-bert-master
https://github.com/xiaopingzhong/bert-finetune-for-classfier
https://github.com/xiaopp123/bert_explain
https://github.com/xingjian-zhang/massw
https://github.com/xingniu/sockeye
https://github.com/xitianxiaofeixue/BERT
https://github.com/xiuyu0000/new_papers_codes/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/xmu-xiaoma666/External-Attention-pytorch
https://github.com/xuetianci/pacit
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.