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/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/eternityyw/tram-benchmark
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/ezelikman/Context-Is-Everything
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/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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.