Abstractive Text Summarization
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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/emanuele-progr/PSSP
https://github.com/enhuiz/torchnmt
https://github.com/enhuiz/transformer-pytorch
https://github.com/ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder
https://github.com/ethancaballero/neural-engineers-first-attempt
https://github.com/eunseo1092/Graduation_project
https://github.com/ewdowiak/Sicilian_Translator
https://github.com/exa-labs/exa-mcp-server
https://github.com/extensive-nlp/TSAI-DeepNLP-END2.0
https://github.com/facebookresearch/GENRE
https://github.com/facebookresearch/atlas
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/facebookresearch/bart_ls
https://github.com/facebookresearch/data2vec_vision
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/facebookresearch/fairseq
https://github.com/facebookresearch/xformers
https://github.com/faizansuhail89/bert
https://github.com/fanchenyou/transformer-study
https://github.com/fancyerii/bert
https://github.com/fciannel/bert_fciannel
https://github.com/feizc/Machine-Translation-Pytorch
https://github.com/felixSchober/ABSA-Transformer
https://github.com/fjiangai/mmapis
https://github.com/fladhak/creative-summ-data
BookSum: A Collection of Datasets for Long-form Narrative Summarization
https://github.com/flxst/nerblackbox
https://github.com/flyliu2017/bert
https://github.com/flyliu2017/bert_modularized
https://github.com/fra-mari/The-Karl-Marx-Press-Review
https://github.com/frankcgq105/BERTCHEN
https://github.com/franknb/Self-attention-DCGAN
https://github.com/franknb/Text-Summarization
https://github.com/freeflyxiaoma/pycorrector
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
https://github.com/fstahlberg/tensor2tensor
https://github.com/fuqiang-git-hub/unilmv1-Paddle
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/fushengwuyu/R-Drop
https://github.com/fwbrandao/Abstractive_Summarisation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.