https://github.com/creat89/SummTriver
Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic 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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic Text Summarization
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Automatic Text Evaluation through the Lens of Wasserstein Barycenters
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
Get To The Point: Summarization with Pointer-Generator Networks
Get To The Point: Summarization with Pointer-Generator Networks
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
A Regularized Framework for Sparse and Structured Neural Attention
Leveraging BERT for Extractive Text Summarization on Lectures
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.