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Searching for Effective Neural Extractive Summarization: What Works and What's Next
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.
Searching for Effective Neural Extractive Summarization: What Works and What's Next
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Get To The Point: Summarization with Pointer-Generator Networks
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Evaluating the Factual Consistency of Abstractive Text Summarization
HaRiM$^+$: Evaluating Summary Quality with Hallucination Risk
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.