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/imcaspar/gpt2-ml
https://github.com/inesylla/energy-disaggregation-DL
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/insigh/THUMT
https://github.com/ionnoant/DS8008-final-project
https://github.com/ishritam/Image-captioning-with-visual-attention
https://github.com/ishtoo1/Text-Summarization-using-Attention
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/ishtoo1/Text-Summarization-using-Pointer-Generator-Model
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/itzprashu1/prashant
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/iwangjian/textsum-gan
Generative Adversarial Network for Abstractive Text Summarization
https://github.com/j40903272/bottom-up-summary
https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/jacobkrantz/NeuralSum
https://github.com/jadore801120/attention-is-all-you-need-pytorch
https://github.com/jageshmaharjan/BERT_Service
https://github.com/jaisenbe58r/NLP-Transformer_Translator
https://github.com/jamesdvance/predicting_clicks
https://github.com/jananiarunachalam/Research-Paper-Summarization
https://github.com/jangjoongkeon/JK
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jbdel/OMG_UMONS_submission
https://github.com/jbshp/gencomparesum
https://github.com/jean-kunz/ml_research_papers
https://github.com/jelifysh/Transformers
https://github.com/jeongchanyoung-1234/De-En-NMT
https://github.com/jessicalopezespejel/gesera
https://github.com/jessie0624/Automatic-Text-Summarization
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer
https://github.com/jiahuanluo/Gated-attention_sum
https://github.com/jiahuei/Self-Critical-SCST-TensorFlow
https://github.com/jiaruncao/BioCopyMechanism
https://github.com/jielin-qiu/mmsum_model
MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
https://github.com/jiminsun/pointer-generator
Get To The Point: Summarization with Pointer-Generator Networks
https://github.com/jinzhenfan/BERT
https://github.com/jmnolte/hccnet
https://github.com/joebluems/Soul_Of_AI
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.