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Text Summarization

473 평가 기록 92 datasets

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.

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.

Dataset기록된 모델 예시MetricPaperCode
Scrambled code + broken (alter)
ROUGE-1: 48.18ROUGE-2: 19.84ROUGE-L: 45.35
논문 코드
OpenAI/o3-mini
ROUGE-1: 60.12ROUGE-2: 54.22ROUGE-L: 57.21
- -
Top Down Transformer (AdaPool) (464M)
ROUGE-1: 51.05ROUGE-2: 23.26ROUGE-L: 46.47
논문 코드
Top Down Transformer (AdaPool) (464M)
ROUGE-1: 50.95ROUGE-2: 21.93ROUGE-L: 45.61
논문 코드
Scrambled code + broken (alter)
ROUGE-1: 48.18ROUGE-2: 19.84ROUGE-L: 45.35
논문 코드
MPNet-multilingual
Spearman Correlation: 31.57
논문 코드
Selfmem
ROUGE-1: 50.30ROUGE-2: 26.70ROUGE-3: 41.60
논문 코드
HAHSum
ROUGE-2: 21.30ROUGE-1: 44.68ROUGE-L: 40.75
논문 -
HSSAS
ROUGE-1: 42.3ROUGE-2: 17.8ROUGE-L: 37.6
논문 -
Transformer+WDrop
ROUGE-1: 33.06ROUGE-2: 11.45ROUGE-L: 28.51
논문 코드
OmniVec2
ROUGE-1: 59.1ROUGE-2: 34.1ROUGE-L: 63.7BertScoreF1: 65.1
논문 -
LexRank (query: step title)
ROUGE-1: 39.6
논문 코드

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