https://github.com/toru34/rush_emnlp_2015
A Neural Attention Model for Abstractive Sentence Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
A Neural Attention Model for Abstractive Sentence Summarization
Unified Language Model Pre-training for Natural Language Understanding and Generation
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
A Regularized Framework for Sparse and Structured Neural Attention
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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.