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/han-shi/SparseBERT
https://github.com/hannibal046/nanorwkv
https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning
https://github.com/harshit158/paper-dots
https://github.com/harvardnlp/annotated-transformer
https://github.com/hfxunlp/transformer
https://github.com/hihellohowareyou/RESREF_Chatbot_data_for_Korean
https://github.com/hiun/learning-transformers
https://github.com/hkunlp/chunkllama
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/hoangtrungchinh/Bert-SQuAD-v2
https://github.com/hrbigelow/transformer-aiayn
https://github.com/hrlinlp/coconet
https://github.com/https-github-com-GiangHoang9912/ernie-gen
https://github.com/huangxt39/BART_on_COVID_dialogue
https://github.com/hubiodatalab/druggen
https://github.com/huffon/factsumm
https://github.com/huggingface/swift-coreml-transformers
https://github.com/huggingface/transformers
https://github.com/hyunwookl/testam
https://github.com/hyunwoongko/transformer
https://github.com/i2r-simmc/i2r-simmc-2020
https://github.com/iamgroot42/mimir
https://github.com/ibm/graph_ensemble_learning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ibm/kpa_2021_shared_task
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis
https://github.com/ibrahim-elsawy/test
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/icewing1996/bert_dep
https://github.com/ictnlp/DiverseNMT
https://github.com/ictnlp/NA-MNMT
https://github.com/ictnlp/PTE-NMT
https://github.com/ictnlp/Seq-NAT
https://github.com/ictnlp/TLAT-NMT
https://github.com/idc-dsi/Arapet
https://github.com/idiotprofessorchen/bert.github.io
https://github.com/iejMac/ScriptWriter
https://github.com/iinemo/lm-polygraph
Mutual Information Alleviates Hallucinations in Abstractive Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.