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/johnnyb1509/2020_VBDI_DL
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jonjoncardoso/jonjoncardoso
Very Large Language Model as a Unified Methodology of Text Mining
https://github.com/joongbo/tta
https://github.com/joytianya/google_bert
https://github.com/jungokasai/billboard
https://github.com/jungokasai/thumb
https://github.com/jungokasai/twist_decoding
https://github.com/junhahyung/bert_finetune
https://github.com/junhahyung/bert_transfer
https://github.com/junnyu/paddle_t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jzhang38/longmamba
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/k-randl/self-explaining_llms
https://github.com/kaituoxu/Speech-Transformer
https://github.com/kaituoxu/SpeechTransformer
https://github.com/karenacorn99/explore-bert
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
https://github.com/karta282950/bert-multiclass
https://github.com/kashgupta/textsummarization
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
https://github.com/kedz/nnsum
https://github.com/keiyamashita111/aaproject
https://github.com/kelly2016/multi-label-bert
https://github.com/keonlee9420/Comprehensive-Transformer-TTS
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/keonlee9420/Stepwise_Monotonic_Multihead_Attention
Online and Linear-Time Attention by Enforcing Monotonic Alignments
https://github.com/kh-kim/simple-nmt
https://github.com/khushsi/Finetuning_BART_for_FACET_Summarization
https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks
https://github.com/kiko441500/google_bert
https://github.com/kingcheng2000/bert
https://github.com/kiyoungkim1/LMkor
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/knotgrass/attention/blob/main/attn/attention.py
https://github.com/kolloldas/torchnlp
https://github.com/kotu931226/classifier_transformer_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.