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/bangoc123/transformer
https://github.com/baoguangsheng/g-transformer
https://github.com/baosongyang/WRD
https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/bartzi/chainer-transformer
https://github.com/basf/chemteb
https://github.com/basile-bron/Artificial-Intelligence-Lexical
https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/bdusell/stack-attention
https://github.com/bearblog/CoreRank
https://github.com/becxer/pointer-generator
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/benjamintaiwo/Attention
https://github.com/bfopengradient/NLP_text_summarization_apps
https://github.com/bhavitvyamalik/bert
https://github.com/bigai-nlco/cream
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
https://github.com/bigscience-workshop/architecture-objective
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/binhetech/bert-application
https://github.com/bkoch4142/attention-is-all-you-need-paper
https://github.com/bluejurand/Kaggle_QA_Google_Labeling
https://github.com/bnitsan/papertweet
https://github.com/bojone/r-drop
https://github.com/bondarchukb/PEGASUS
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/bourrel/French-News-Clustering
Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts
https://github.com/brainsqueeze/text2vec
https://github.com/brandontrabucco/scaled_dot_product_attention
https://github.com/brightmart/bert_customized
https://github.com/brightmart/bert_original
https://github.com/brsarah20/Alphafold2
https://github.com/brynnchernosky/seq2seq
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
https://github.com/bshantam97/Attention_Based_Networks
https://github.com/btobab/R-Drop
https://github.com/bunsenfeng/factkb
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
https://github.com/buschmo/Simple-German-Corpus
https://github.com/bwallace/RCT-summarization-data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.