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/BasileBron/Artificial-Intelligence-Lexical
https://github.com/Belval/NRTR
https://github.com/Bhavnicksm/vanilla-transformer-jax
https://github.com/BonanKou/ASSORT-Automatic-Summarization-of-Stack-Overflow-Posts
https://github.com/Bradley-Butcher/Conformers
https://github.com/Bread-and-Code/Text-Summarization
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
https://github.com/BrianPulfer/PapersReimplementations
https://github.com/CGCL-codes/naturalcc
https://github.com/CLUEbenchmark/CLGE
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
https://github.com/CVxTz/music_genre_classification
https://github.com/Caesarzhang/bert-zh
https://github.com/CaseyPan/Chinese-Preprocessing
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch
https://github.com/Charliesgithub20221030/BERT
https://github.com/Chen-Wang-CUHK/DialSum-with-SUFM-and-FR
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Chinnu1103/Machine-Translation-using-Transformers
https://github.com/Chonwai/Learning_BERT
https://github.com/Colanim/BERT_STS-B
https://github.com/DJJune/Transformer
https://github.com/DanielaStep/SC
Scoring Sentence Singletons and Pairs for Abstractive Summarization
https://github.com/DanielaStep/sentence_similarity_rouge
Scoring Sentence Singletons and Pairs for Abstractive Summarization
https://github.com/DanieleVeri/deep_comedy
https://github.com/DataScienceNigeria/SummAE-from-Google-Brain-and-MIT-CSAIL
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
https://github.com/Dave870907/Transformer
https://github.com/DeligientSloth/QQsim
https://github.com/DeligientSloth/bert-tensorflow
https://github.com/Delta-ML/delta
https://github.com/DeokO/bert-excercise-ongoing
https://github.com/DevSinghSachan/multilingual_nmt
https://github.com/Di-Is/stylegan2-ada-pytorch
https://github.com/Doffery/BERT-Sentiment-Analysis-Amazon-Review
https://github.com/EdinburghNLP/XSum
https://github.com/Eeun-ju/NLP-study
https://github.com/ElmiraGhorbani/Abstract-Text-Summarization
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.