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/Misoknisky/Bert-MultiGpu
https://github.com/Mleader2/text_scalpel
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/MorenoLaQuatra/bart-it
https://github.com/MseXing/CopyNet
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
https://github.com/NVIDIA/sentiment-discovery
https://github.com/Neoanarika/torchexplainer
https://github.com/Nimesh-Patel/BERT
https://github.com/Nimesh-Patel/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/NirmalenduPrakash/Document-Summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/NirmalenduPrakash/DocumentSummarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/Nstats/bert_MRC
https://github.com/Nstats/bert_senti_analysis_ch
https://github.com/Nstats/my_bert
https://github.com/OPAYA/Transformer
https://github.com/Osobarako/alduswarrensewell
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/ernie_gen/modeling.py
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_translation/transformer
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/text_summarization/pointer_summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/bigbird
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/Paleontolog/pointer_summarizer
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/Pavyel/Novelty-Detection
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
https://github.com/Pezhin/best_yelp_review_project_ever
https://github.com/PrideLee/sentiment-analysis
https://github.com/PrimerAI/blanc
https://github.com/Rami97rgb/French-to-English-Translator
https://github.com/Remiphilius/PoemesProfonds
https://github.com/RenXiangyuan/tf_bert
https://github.com/Rishit-dagli/Fast-Transformer
https://github.com/Rudedaisy/attention-is-all-you-need-pytorch
https://github.com/Rutts07/Summarization-using-Pointer-Generator-Networks
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/SCismycat/bert_code_view
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.