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/xydaytoy/BMI-NMT
https://github.com/yahah100/text_summarization
Pretraining-Based Natural Language Generation for Text Summarization
https://github.com/yale-lily/ehrkit-2022
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
https://github.com/yangjianxin1/longqlora
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/yangyucheng000/University/tree/main/model-1/pegasus
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/yangyucheng000/University/tree/main/model-1/pegasus_x
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
https://github.com/yanqi1811/attention-is-all-you-need
https://github.com/yaushian/Unparalleled-Text-Summarization-using-GAN
Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks
https://github.com/yeeeqichen/Bert
https://github.com/yinghanlong/SRtransformer
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
https://github.com/yingtaomj/Learning-towards-Abstractive-Timeline-Summarization
https://github.com/yinyueqin/denserewardrlhf-ppo
Preference-grounded Token-level Guidance for Language Model Fine-tuning
https://github.com/yiyc-kor/bert-study
https://github.com/yizhongw/tk-instruct
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ykrmm/ICLR_2020
https://github.com/ykrmm/TREMBA
https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ymcui/chinese-llama-alpaca-2
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/yoongtr/Grammar-Error-Correction-with-OpenNMT-tf
https://github.com/ypeleg/Fastformer-Keras
https://github.com/ypeleg/MinimalIsAllYouNeed
https://github.com/yuanmu97/secure-transformer-inference
https://github.com/yueyongjiao/Transformer-Contextual
https://github.com/yueyongjiao/attention_is_all_you_need_pytorch
https://github.com/yuhangT/tf_bert
https://github.com/yumoh/speech-keras
https://github.com/yunzhusong/AAAI20-PORLHG
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
https://github.com/yydai/bert_test
https://github.com/zankner/Transformer
https://github.com/zapplea/bert
https://github.com/zbp-xxxp/R-Drop-Paddle
https://github.com/zer0sh0t/artificial_intelligence/tree/master/transformer
https://github.com/zetangforward/lcm_stack
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
https://github.com/zhang-huihui/git-repository
https://github.com/zhangbo2008/best_pytorch_transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.