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/lucidrains/linear-attention-transformer
https://github.com/lucidrains/marge-pytorch
https://github.com/lucidrains/memory-compressed-attention
https://github.com/lucidrains/memory-transformer-xl
https://github.com/lucidrains/stylegan2-pytorch
https://github.com/lucidrains/x-transformers
https://github.com/luckynozomi/PPI_Bert
https://github.com/lucylow/En_francais_si_vous_plait-
https://github.com/lukemelas/PyTorch-Pretrained-ViT
https://github.com/lululuyi/longheads
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/luomancs/retriever_reader_for_okvqa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/lvyufeng/bert4ms
https://github.com/lvyufeng/mindspore-nlp-tutorial
https://github.com/lvyufeng/pointer_summarizer_mindspore
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/lyon-nlp/mteb-french
https://github.com/lyqcom/mbart_mindspore
BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
https://github.com/m3hrdadfi/news-headline-generation
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/m3hrdadfi/wiki-summary
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/maanvithag/thinkai
https://github.com/magahub/songrnn
https://github.com/magic282/NeuSum
Neural Document Summarization by Jointly Learning to Score and Select Sentences
https://github.com/maheshmylavarapu0057/QueryBasedSummarisation
Diversity driven Attention Model for Query-based Abstractive Summarization
https://github.com/majing2019/transformer
https://github.com/majumderb/sanskrit-ocr
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
https://github.com/manavkapadnis/enigma_argmining
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis
https://github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR
https://github.com/manshri/tesum
https://github.com/maria-liakata-nlp-group/wassos
Unsupervised Opinion Summarization as Copycat-Review Generation
https://github.com/maroxtn/tun-sentiment
https://github.com/maszhongming/Effective_Extractive_Summarization
Searching for Effective Neural Extractive Summarization: What Works and What's Next
https://github.com/maxjcohen/transformer
https://github.com/maxmatical/pytorch-projects
https://github.com/maxudong1993/mybert
https://github.com/maxwell1447/fairseq
https://github.com/mayoor/attention_network_experiments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.