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/abhaskumarsinha/MinimalGPT
https://github.com/abhiagg92/bert_sentiment
https://github.com/abhilash1910/LongPegasus
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/abhilashreddys/Fake-News-Article
https://github.com/abhisha1991/w266_final_project
https://github.com/abisee/cnn-dailymail
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/abmitra84/Machine_Translation
https://github.com/abrazinskas/Copycat-abstractive-opinion-summarizer
Unsupervised Opinion Summarization as Copycat-Review Generation
https://github.com/abstatic/ire_project18
https://github.com/absummarization/absummarization.github.io
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/acmi-lab/pretraining-with-nonsense
Does Pretraining for Summarization Require Knowledge Transfer?
https://github.com/adamklec/copynet
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
https://github.com/adi2103/AML-CoVe
https://github.com/adrian9631/TextSumma
https://github.com/af-ai-center/bert
https://github.com/af-ai-center/nerblackbox
https://github.com/affjljoo3581/GPT2
https://github.com/agentdr1/la_mil
https://github.com/ahmetumutdurmus/awd-lstm
https://github.com/ahsanabbas123/Transformer
https://github.com/aikawasho/BertSum
https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/aishj10/swap-net
https://github.com/akanyaani/gpt-2-tensorflow2.0
https://github.com/akanyaani/minGPTF
https://github.com/akashe/Python-Code-Generation
https://github.com/algharak/BERTenhance
https://github.com/alibaba-damo-academy/FunASR
https://github.com/alibabasglab/frcrn
https://github.com/alililia/ms_contrib/tree/main/ascend_transformer
https://github.com/alililia/ms_contrib/tree/main/gpu_transformer
https://github.com/alimpiev/embeddings_persistent_homology
https://github.com/allenai/allennlp-models
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.