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/cadia-lvl/fastspeech2
https://github.com/cahya-wirawan/indonesian-language-models
https://github.com/camlaedtke/NLP
https://github.com/caoyujiALgLM/NLP
https://github.com/caoyujiALgLM/NLP-progress
https://github.com/carrliitos/NLPInformationExtraction
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/chakravarthi-v/Polaroid-1
https://github.com/chalothon/BERT_Practice
https://github.com/chalothon/lecture_extractive_sum
Leveraging BERT for Extractive Text Summarization on Lectures
https://github.com/chancefocus/pixiu
FinBen: A Holistic Financial Benchmark for Large Language Models
https://github.com/chandu7077/mybert
https://github.com/chen-xiong-yi/OwnBERT
https://github.com/chengchingwen/transformers.jl
https://github.com/chiayewken/bert-qa
https://github.com/chrisleunglokhin/Capstone-BERT
https://github.com/chrisociepa/allamo
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/christianspybrook/article_recommender
https://github.com/cliang1453/camero
https://github.com/climsocana/tecb-de
https://github.com/cloudmcloudyo/capstone
https://github.com/coco60/bert-test
https://github.com/colorfulscoop/tfdlg
https://github.com/complementizer/news-tls
Examining the State-of-the-Art in News Timeline Summarization
https://github.com/conceptofmind/LaMDA-pytorch
https://github.com/conceptofmind/lamda-rlhf-pytorch
https://github.com/copenlu/ALPS_2021
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
https://github.com/coronazap/bert_client
https://github.com/cosmoquester/2021-dialogue-summary-competition
https://github.com/cosmoquester/seq2seq
https://github.com/cosmoquester/transformers-bart-pretrain
https://github.com/cospplay/bert-master
https://github.com/coxy1989/tfmr
https://github.com/cpm0722/transformer_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.