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Abstractive Text Summarization
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- 평가표
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Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.
Source: Generative Adversarial Network for Abstractive Text Summarization
Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vishnuanil122/Bert-Binary-Text-Classifier
https://github.com/voidforall/DialSummar
https://github.com/volcengine/vegiantmodel
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vpj/jax_transformer
https://github.com/vulus98/rethinking-attention
https://github.com/vvvm23/TchAIkovsky-Legacy
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/webis-de/summary-workbench
https://github.com/whr94621/NJUNMT-pytorch
https://github.com/why2000/DuReader-bert
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
https://github.com/xuzhezhaozhao/bert_reading
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.